Value and risk management system for multi-enterprise organization

ABSTRACT

An automated method and system ( 100 ) for defining, measuring and continuously monitoring the matrix of value and the matrix of risk for a multi-enterprise commercial organization. A complete matrix of value is developed for each enterprise in the organization using predictive models and vector creation algorithms. The matrices of enterprise value are then used to support the creation of scenarios that contain all enterprise risk factors. A series of scenarios under both normal and extreme conditions are then developed in order to develop a complete matrix of risk for each enterprise in the organization and the organization as a whole. The information from these matrices is then used to calculate and display the matrix of value for the organization, the matrix of risk for the organization and the efficient frontier for organization financial performance. Forecast changes to the organization and its environment are then mapped to the matrices of value and risk for the organization and analyzed using probabilistic simulation models.

CROSS REFERENCE TO RELATED APPLICATIONS AND PATENTS

[0001] This subject matter of this application is related to applicationSer. No. 09/421,553, filed Oct. 20, 1999, application Ser. No.09/775,561 filed Feb. 5, 2001, application Ser. No. 09/953,148 filedSep. 17, 2001, U.S. Pat. No. 5,615,109 for “Method of and System forGenerating Feasible, Profit Maximizing Requisition Sets”, by Jeff S.Eder, and U.S. Pat. No. 6,321,205 for “Method of and System for Modelingand Analyzing Business

BACKGROUND OF THE INVENTION

[0002] This invention relates to a method of and system for defining,measuring and continuously optimizing the matrices of value and risk fora multi-enterprise commercial organization.

[0003] Managing a business in a manner that creates long term value is acomplex and time-consuming undertaking. This task is complicated by thefact that traditional financial and risk management systems do notprovide sufficient information for managers in the Knowledge Economy tomake the proper decisions. Traditional financial systems are alsolimited in their ability to support the effective management ofmulti-enterprise organizations like “virtual value chains” andcorporations with multiple operating companies.

[0004] Many have noted that traditional financial systems like thegeneral ledger accounting system are driving modern managers to make thewrong decisions and the wrong investments. Accounting systems are“wrong” for several reasons. One of the most obvious reasons—they tracktangible assets while ignoring intangible assets. Intangible assets suchas the skills of the employees, intellectual property, businessinfrastructure, databases, management processes, relationships withcustomers and relationships with suppliers are not measured with currentaccounting systems. This oversight is critical because the success of anenterprise is determined more by its ability to use its intangibleassets than by its ability to amass and control the physical ones thatare tracked by traditional accounting systems. The absence of intangibleasset information is particularly notable in high technology companiesthat are highly valued for their intangible assets and their options toenter new markets.

[0005] Even when intangible assets have been considered in financialmanagement systems, the limitations in the existing methodology haveseverely restricted the utility of the information that has beenproduced. All known prior efforts to value individual intangible assetshave been restricted to independent valuations of different types ofassets with only limited attempts to measure the actual impact of theasset on the enterprise that owns it. Some of the intangible assets thathave been valued separately in this fashion are: brand names, customersand intellectual property. Problems associated with the known methodsfor valuing individual intangible assets include:

[0006] 1. Interaction between intangible assets are generally ignored.For example, the value of a brand name is in part a function of thecustomers that use the product—the more prestigious the customers, thestronger the brand name. In a similar fashion the stronger the brandname, the more likely it will be that customers will stay a long time.Because of this high level of interaction, valuing either of theseassets in isolation will not provide a meaningful valuation; and;

[0007] 2. The value of an intangible asset is a function of the benefitthat it provides the enterprise. Therefore, measuring the value of anintangible asset requires a method for measuring the actual impact ofthe asset on the enterprise—something that is missing from all knownexisting methods.

[0008] Another aspect of the increasing importance of “intangible”assets is the fact that the primary risks faced by most companies havenow shifted from hard asset damage (fire, flood, etc.) to soft assetimpairment or loss. A recent study found that between June 1993 and May1998, ten percent of the Fortune 1000 lost more than one quarter (25%)of their total shareholder value in one month. Almost two thirds ofthese “large losses” were caused by problems related to intangibleassets.

[0009] The deficiencies of traditional accounting reports exacerbate thedifficulty companies face when reporting problems with intangible assetsbecause:

[0010] 1. The absence of regular reporting means that all problems withintangible assets come “out of the blue”; and

[0011] 2. The absence or regular reporting makes it difficult to monitorcompany efforts to correct the problems.

[0012] Given the absence of reporting on many of the intangible assetsdriving the success of companies in the Knowledge Economy, it should notbe surprising to learn that traditional accounting systems are alsodeficient in reporting significant information relevant to the liabilityside of the balance sheet. Traditional financial statements footnote orin some cases ignore large potential liabilities including: loss fromlitigation, environmental clean-up costs and shortfalls on leasingrevenues. The absence of routine reporting on these 6risks does notalter the fact that they have a material, negative impact on the valueof the company that has these risks. Recent studies completed at OxfordUniversity have confirmed that “off balance sheet risk” has a negativeimpact on market value for firms that have these risks.

[0013] This negative impact of these risks on market value can besubstantial. A recently completed study found that exposure to future,un-booked liabilities for environmental cleanup reduced share price byan average of 16% for electric utilities targeted by the Clean Air ActAmendments of 1990. It is worth noting that as more information becamepublic regarding the actual cost of the environmental cleanup andpollution abatement the reduction in share price moderated. Atransparent analysis of the liability associated with the environmentalcleanup would have given the market the information required to morerapidly reach the proper conclusion regarding the impact of these newcosts.

[0014] In addition to risks from intangible asset impairment andunrecognized liabilities, companies face other risks that are morereadily analyzed. These risks are shown in Table 1. TABLE 1 Risks thatare typically analyzed 1. Foreign exchange risk; 2. Interest rate risk;3. Portfolio risk; 4. Credit risk; and 5. Commodity price risk;

[0015] These risks are usually analyzed using a standardized riskanalysis product such as Dun and Bradstreet's Risk Assessment Manager™for credit risk and Barra's Cosmos™ System for portfolio risk. Theanalyses of the risks listed in Table 1 are generally completed inisolation so their impact on the overall firm is not clear andopportunities for natural “self hedging” are not readily apparent.Another shortcoming of traditional risk management systems is theirinability to plan for “once in a lifetime events”. Because traditionalrisk management systems are driven by a statistical analysis of priorhistory, they are generally limited to dealing with events that varywithin parameters that have already been experienced. The problem withthis is that most large losses are caused by events that fall outsidethe bounds of normal experience (i.e. hundred-year floods andonce-in-a-lifetime events).

[0016] The limitations of the general ledger accounting systems andtraditional risk analysis systems discussed in the preceedingparagraphs—lack of information about intangible assets, lack ofinformation about off balance sheet risk, isolated risk analyses and theinability to plan for extreme events—extend to the all known efforts toanalyze and/or simulate the impact of changes in the business onfinancial performance and risk. The lack of detailed information onintangible assets and off balance sheet risk has also limited simulationproducts such as the Dynamic Financial Analysis (DFA) and the SmallBusiness Financial Manager to projecting the impact of changes inoperation on financial performance. Given the growing importance ofintangible assets to financial performance and risk, the utility ofthese systems is very limited. In a similar manner, the lack ofquantitative information on the impact of intangibles and risk onfinancial performance has limited the usefulness of simulation productssuch as Tango that incorporate generic information regardingintangibles.

[0017] In addition to the limitations of the individual systemsdescribed above, there are no known systems that perform the integratedanalysis of value and risk at the enterprise or multi-enterpriseorganization level. In so far as business value and business risk areintimately inter-related, this limitation severely restricts theusefulness of stand-alone systems for managing enterprise risk andenterprise financial performance. Another limitation of even the mostadvanced enterprise risk and enterprise financial management systems isthat they do not provide any information about expected value given therisks facing the enterprise or organization. By way of contrast,portfolio theory for stock market investments are used to guideinvestment managers to reasonable expectations regarding expectedreturns given the riskiness of their portfolio. The efficient frontierin modem portfolio theory is defined by the maximum expected return forevery level of portfolio risk. A system capable of identifying theefficient frontier for managing a corporate portfolio of assets, optionsand risks would alleviate this problem. It is worth noting at this pointthat simple portfolio analysis and optimization systems are available.Unfortunately, these systems do not address:

[0018] 1. value at the enterprise or organization level,

[0019] 2. the five different ways in which business value is created(see segment of value definitions);

[0020] 3. risk at the enterprise or organization level,

[0021] 4. the six different types of risk (risk by segment of value plusevent risk);

[0022] 5. the inter-relationship between value and risk; and/or

[0023] 6. the complex inter-relationships between different assets (akaelements of value).

[0024] There is no question that a system to identify and help managethe efficient frontier for a commercial enterprise is an enormousadvance over the financial and risk management systems available today.Unfortunately, this advance still does not address the needs of some ofthe more dynamic corporations in the economy including companiescontaining multiple enterprises and companies working collaborativelywithin virtual value chains.

[0025] One of the least publicized impacts of the Internet on globalcommerce has been the accelerated the trend toward the “virtualintegration” of companies in different locations and differentindustries. Companies can now join together in a short period of timewith very little investment to form a “virtual value chain” fordelivering products and services to consumers. The virtual value chainmay appear to the consumer as a single entity, when in reality a numberof enterprises from different continents and industries may have joinedtogether to complete the preparation and delivery of the good or servicethat is ultimately being purchased. Virtual value chains allow each firmin the value chain to focus on their own specialty, be it manufacturing,design, distribution or marketing while reaping the benefits of theincreased scale and scope inherent in the alliance. Enabled by the lowcost communication capability provided by the Internet, the virtualvalue chain is really just an extreme form of a phenomenon that has beensweeping American industry for many years—the electronic linkage ofbusinesses. These electronic linkages have been given many namesincluding value web, value net and value chain. We will use the termvalue chain to describe them.

[0026] The existence of corporations with more than one enterprise orcompany preceed the advent of the Internet. Multi-enterprisecorporations include many of the most successful corporations in theworld. In fact, General Electric, currently the most valuablecorporation in the world consists of several distinct businessesincluding GE Capital, NBC, GE Plastics and GE Appliances. In spite oftheir success, there are no known efforts to systematically measure,manage and optimize the value and risk associated with the operation ofa multi-company corporations. We will use the term multi-enterpriseorganization to describe both value chains and multi-companycorporations.

[0027] In light of the preceding discussion, it is clear that it wouldbe desirable to have an automated system that measured and displayed thefull spectrum of assets, options and risks within a multi-enterpriseorganization. Ideally, this system would forecast organizational risk inboth normal and extreme situations and would define the efficientfrontier for organization financial performance.

SUMMARY OF THE INVENTION

[0028] It is a general object of the present invention to provide anovel and useful system for defining, measuring and optimizing thematrices of value and risk for a multi-enterprise organization thatovercomes the limitations and drawbacks of the existing art that weredescribed previously.

[0029] A preferable object to which the present invention is applied isfully quantifying and then optimizing the assets, risks and optionsassociated with operating a multi-enterprise commercial organization.Quantification and optimization are enabled by:

[0030] 1) Systematically analyzing up to five segments of value—currentoperation, real options/contingent liabilities, derivatives, excessfinancial assets and market sentiment for each enterprise in theorganization;

[0031] 2) Systematically analyzing and valuing all the elements ofvalue, tangible and intangible, that affect the segments of value foreach enterprise in the organization;

[0032] 3) Systematically analyzing and valuing all the external factorsthat affect the segments of value for each enterprise in theorganization;

[0033] 4) Developing an understanding of the risk associated withexternal factors, elements of value and event risks by segment of valueunder both normal and extreme conditions for each enterprise in theorganization;

[0034] 5) Integrating information and insights from asset managementsystems (i.e. Customer Relationship Management, Brand Management, etc.),asset risk management systems (credit risk, currency risk, etc.) andbusiness intelligence systems for each enterprise in the organization;and

[0035] 6) Summing the enterprise analyses as required to complete thematrices of value and risk and define the efficient frontier fororganization financial performance.

[0036] While the preferred embodiment of the novel system for definingand measuring the matrices of organizational value and risk analyzes allfive segments of value, the system can operate when one or more of thesegments of value are missing for one or more enterprises and/or for theorganization as a whole. For example, the organization may be a valuechain that does not have a market value in which case there will be nomarket sentiment to evaluate. Another common situation would be amulti-company corporation that has no derivatives and/or excessfinancial assets in most of the enterprises (or companies) within it.

[0037] As detailed later, the segments of value that are present in eachenterprise are defined in the system settings table (140). Virtually allpublic companies will have at least three segments of value, currentoperation, real options and market sentiment. However, it is worthnoting only one segment of value is required per enterprise foroperation of the system. Because most corporations have only one tradedstock, multi-company corporations will generally define an enterprisefor the “corporate shell” to account for all market sentiment. This“corporate shell” enterprise can also be used to account for any jointoptions the different companies within the corporation may collectivelypossess. The system is also capable of analyzing the value of theorganization without considering risk. However, the system needs tocomplete the value analyses before it can complete the analysis of allorganization risks.

[0038] The system of the present invention has the added benefit ofeliminating a great deal of time-consuming and expensive effort byautomating the extraction of data from the databases, tables, and filesof existing computer-based corporate finance, operations, humanresource, supply chain, web-site and asset management system databasesas required to operate the system. In accordance with the invention, theautomated extraction, aggregation and analysis of data from a variety ofexisting computer-based systems significantly increases the scale andscope of the analysis that can be completed. The system of the presentinvention further enhances the efficiency and effectiveness of theanalysis by automating the retrieval, storage and analysis ofinformation useful for valuing elements of value and segments of valuefrom external databases, external publications and the Internet.

[0039] Uncertainty over which method is being used for completing thevaluation and the resulting inability to compare different valuations iseliminated by the present invention by consistently utilizing the sameset of valuation methodologies for valuing the different segments ofvalue as shown in Table 2. TABLE 2 Segment of organization Valuationvalue by enterprise methodology Current-operation value Income valuation(COPTOT) - value of operation that is developing, making, supplying andselling products and/or services Excess net financial assets Total NetFinancial Assets valued (aka Excess financial assets) using GAAP -(amount required to support current operation) Real Options & ContingentReal option algorithms and Liabilities (aka Real options) optionalallocation of industry options Derivatives - includes all Risk NeutralValuation hedges, swaps, swaptions, options and warrants MarketSentiment Market Value* − (COPTOT + Σ Real Option Values + ΣDerivatives + Σ Excess Financial Assets)

[0040] The market value of the organization is calculated by combiningthe market value of all debt and equity as shown in Table 3. Element andexternal factor values are calculated based on the sum of their relativecontributions to each segment of value for each enterprise. TABLE 3Organization Market Value = Σ Market value of equity for all enterprises− Σ Market value of debt for all enterprises

[0041] Consultants from McKinsey & Company recently completed a threeyear study of companies in 10 industry segments in 12 countries thatconfirmed the importance of intangible elements of value as enablers ofnew business expansion and profitable growth. The results of the study,published in the book The Alchemy of Growth, revealed three commoncharacteristics of the most successful businesses in the currenteconomy:

[0042] 1. They consistently utilize “soft” or intangible assets likebrands, customers and employees to support business expansion;

[0043] 2. They systematically generate and harvest real options forgrowth; and

[0044] 3. Their management focuses on three distinct “horizons”—shortterm (1-3 years), growth (3-5 years out) and options (beyond 5 years).

[0045] The experience of several of the most important companies in theU.S. economy, e.g. IBM, General Motors and DEC, in the late 1980s andearly 1990s illustrate the problems that can arise when intangible assetinformation is omitted from corporate financial statements and companiesfocus only on the short term horizon. All three companies were showinglarge profits using current accounting systems while their businesseswere deteriorating. If they had been forced to take write-offs when thedeclines in intangible assets were occurring, the problems would havebeen visible to the market and management would have been forced to actto correct the problems much more quickly than they actually did. Thesedeficiencies of traditional accounting systems are particularlynoticeable in high technology companies that are highly valued for theirintangible assets and their options to enter growing markets rather thantheir tangible assets.

[0046] The utility of the valuations produced by the system of thepresent invention are further enhanced by explicitly calculating theexpected longevity of the different elements of value as required toimprove the accuracy and usefulness of the valuations.

[0047] As shown in Table 2, real options and contingent liabilities arevalued using real option algorithms. Because real option algorithmsexplicitly recognize whether or not an investment is reversible and/orif it can be delayed, the values calculated using these algorithms aremore realistic than valuations created using more traditional approacheslike Net Present Value. The use of real option analysis for valuinggrowth opportunities and contingent liabilities (hereinafter, realoptions) gives the present invention a distinct advantage overtraditional approaches to enterprise financial management.

[0048] The innovative system has the added benefit of providing a largeamount of detailed information to the organization users concerning bothtangible and intangible elements of value by enterprise. Becauseintangible elements are by definition not tangible, they can not bemeasured directly. They must instead be measured by the impact they haveon their surrounding environment. There are analogies in the physicalworld. For example, electricity is an “intangible” that is measured bythe impact it has on the surrounding environment. Specifically, thestrength of the magnetic field generated by the flow of electricitythrough a conductor turns a motor and the motion of this motor is usedto determine the amount of electricity that is being consumed.

[0049] The system of the present invention measures intangible elementsof value by identifying the attributes that, like the magnetic field,reflect the strength of the element in driving segments of value(current operation, excess financial assets, real options, derivatives,market sentiment) and/or components of value within the currentoperation (revenue, expense and change in capital) and are relativelyeasy to measure. Once the attributes related to the strength of eachelement are identified, they can be summarized into a single expression(a composite variable or vector) if the attributes don't interact withattributes from other elements. If attributes from one element drivethose from another, then the elements can be combined for analysisand/or the impact of the individual attributes can be summed together tocalculate a value for the element. In the preferred embodiment, vectorsare used to summarize the impact of the element attributes. The vectorsfor all elements are then evaluated to determine their relativecontribution to driving each of the components of value and/or each ofthe segments of value. The system of the present invention calculatesthe product of the relative contribution and the forecast longevity ofeach element to determine the relative contribution to each of thecomponents of value to an overal value. The contribution of each elementto each component of value are then added together to determine thevalue of the current operation contribution of each element (see Table5). The contribution of each element to the enterprise is thendetermined by summing the element contribution to each segment of value.The organization value is then calculated by summing the value all theenterprises within the organization.

[0050] The method for tracking all the elements of value and externalfactors for a business enterprise provided by the present inventioneliminates many of the limitations associated with current systems forfinancial management and risk management that were described previously.In addition to supporting the identification and display of theefficient frontier, the system of the present invention will alsofacilitate: analysis of potential mergers and acquisitions, evaluationof asset purchases/disposals, rating the ability of the organization tore-pay debt and monitoring the performance of outside vendors who havebeen hired boost the value of one or more elements of value (i.e.advertising to increase brand value).

[0051] To facilitate its use as a tool for financial management, thesystem of the present invention produces reports in formats that aresimilar to the reports provided by traditional accounting systems.Incorporating information regarding all the elements of value is justone of the ways the system of the present invention overcomes thelimitations of existing systems. Other advances include:

[0052] 1. The integrated analysis of all the sources of value and risk,

[0053] 2. The automated analysis of risk under both normal and extremeconditions, and

[0054] 3. The automated identification and display of the efficientfrontier for organization financial performance.

[0055] By providing an real-time financial insight to personnel in theorganization, the system of the present invention enables the continuousoptimization of management decision making across the entireorganization.

BRIEF DESCRIPTION OF DRAWINGS

[0056] These and other objects, features and advantages of the presentinvention will be more readily apparent from the following descriptionof the preferred embodiment of the invention in which:

[0057]FIG. 1 is a block diagram showing the major processing steps ofthe present invention;

[0058]FIG. 2 is a diagram showing the files or tables in the applicationdatabase (50) of the present invention that are utilized for datastorage and retrieval during the processing in the innovative system formulti-enteprise organization analysis and optimization;

[0059]FIG. 3 is a block diagram of an implementation of the presentinvention;

[0060]FIG. 4 is a diagram showing the data windows that are used forreceiving information from and transmitting information to the user (20)during system processing;

[0061]FIG. 5A, FIG. 5B, FIG. 5C, FIG. 5D, FIG. 5E, FIG. 5F and FIG. 5Gare block diagrams showing the sequence of steps in the presentinvention used for specifying system settings and for initializing andoperating the data bots that extract, aggregate, store and manipulateinformation utilized in system processing by enterprise;

[0062]FIG. 6A, FIG. 6B and FIG. 6C are block diagrams showing thesequence of steps in the present invention used for analyzing the valueassociated with the organization by enterprise;

[0063]FIG. 7 is a block diagram showing the sequence of steps in thepresent invention used for analyzing the risk associated with theorganization by enterprise;

[0064]FIG. 8 is a block diagram showing the sequence in steps in thepresent invention used in defining and displaying the matrix of value,the matrix of risk and the efficient frontier for the organization;

[0065]FIG. 9 is a diagram showing how the enterprise matrices of valuecan be combined to calculate the organizational matrix of value; and

[0066]FIG. 10 is a diagram showing how the enterprise matrices of riskcan be combined to calculate the organizational matrix of risk;

[0067]FIG. 11 is a sample Value Maps Report from the present inventionshowing the calculated value for the segments of value, the elements ofvalue and the external factors for the organization on the valuationdate;

[0068]FIG. 12 is a sample report showing the efficient frontier forOrganization XYZ and the current position of XYZ relative to theefficient frontier; and

[0069]FIG. 13 is a sample report showing the efficient frontier forOrganization XYZ, the current position of XYZ relative to the efficientfrontier and the forecast of the new position of XYZ relative to theefficient frontier after user specified changes are implemented;

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0070]FIG. 1 provides an overview of the processing completed by theinnovative system for defining, measuring and continuously monitoringthe matrices of value and risk for a multi-enterprise organization. Inaccordance with the present invention, an automated method of and system(100) for producing the matrices of value and risk for amulti-enterprise commercial organization is provided. Processing startsin this system (100) with the specification of system settings and theinitialization and activation of software data “bots” (200) thatextract, aggregate, manipulate and store the data and user (20) inputrequired for completing system processing. This information is extractedvia a network (45) from: a basic financial system database (5), anoperation management system database (10), a web site transaction logdatabase (12), a human resource information system database (15), a riskmanagement system database (17), an external database (25), an advancedfinancial system database (30), a asset management system database (35),a supply chain system database (37) and the Internet (40) for eachenterprise in the organization. These information extractions andaggregations may be influenced by a user (20) through interaction with auser-interface portion of the application software (700) that mediatesthe display, transmission and receipt of all information to and frombrowser software (800) such as the Microsoft Internet Explorer in anaccess device (90) such as a phone, pda or personal computer that theuser (20) interacts with. While only one database of each type (5, 10,12, 15, 17, 25, 30, 35 and 37) is shown in FIG. 1, it is to beunderstood that the system (100) will extract data from at least onedatabase of each type via the network (45) for each enterprise withinthe organization. While the data from multiple asset management systemscan be utilized in the analysis of each element of value completed bythe system of the present invention, the preferred embodiment of thepresent invention contains one asset management system for each elementof value being analyzed for each enterprise within the organization.Asset management systems can include: customer relationship managementsystems, partner relationship management systems, channel managementsystems, knowledge management systems, visitor relationship managementsystems, intellectual property management systems, alliance managementsystems, process management systems, brand management systems, workforcemanagement systems, human resource management systems, email managementsystems, IT management systems and/or quality management systems. Asdefined for this application, asset management system data includes allunclassified text and multi-media data within an enterprise ororganization. Automating the extraction and analysis of data from eachasset management system ensures that every asset—tangible orintangible—is considered within the overall financial framework for theorganization. It should also be understood that it is possible tocomplete a bulk extraction of data from each database (5, 10, 12, 15,17, 25, 30, 35 and 37) and the Internet 40 via the network (45) usingpeer to peer networking and data extraction applications such as DataTransformation Services from Microsoft or the Power Center fromInformatica before initializing the data bots. The data extracted inbulk could be stored in a single datamart, a data warehouse or a storagearea network where the data bots could operate on the aggregated data.

[0071] All extracted information is stored in a file or table(hereinafter, table) within an application database (50) as shown inFIG. 2 or an exchange database (51) as shown in FIG. 10. The applicationdatabase (50) contains tables for storing user input, extractedinformation and system calculations including a system settings table(140), a metadata mapping table (141), a conversion rules table (142), abasic financial system table (143), an operation system table (144), ahuman resource system table (145), an external database table (146), anadvanced finance system table (147), a asset system table (148), a botdate table (149), a keyword table (150), a classified text table (151),a geospatial measures table (152), a composite variables table (153), anindustry ranking table (154), an element definition table (155), asegment definition table (156), a cluster ID table (157), an elementvariables table (158), a vector table (159), a bot table (160), a cashflow table (161), a real option value table (162), a vector table (163),a report table (164), an risk reduction purchase table (165), anenterprise sentiment table (166), a value driver change table (167), asimulation table (168), an external factor definition table (169), astatistics table (170), a scenarios table (171), a web log data table(172), a risk reduction products table (173), a supply chain systemtable (174), an optimal mix table (175), a risk system table (176), anxml summary table (177), a generic risk table (178), a financialforecasts table (179), a semantic map table (180), a frame definitiontable (181) a factor variables table (182) and an analysis definitiontable (183). The application database (50) can optionally exist as adatamart, data warehouse or storage area network. The system of thepresent invention has the ability to accept and store supplemental orprimary data directly from user input, a data warehouse or otherelectronic files in addition to receiving data from the databasesdescribed previously. The system of the present invention also has theability to complete the necessary calculations without receiving datafrom one or more of the specified databases. However, in the preferredembodiment all required information is obtained from the specified datasources (5, 10, 12,15, 17, 25, 30, 35, 37 and 40) for each enterprise inthe organization.

[0072] As shown in FIG. 3, the preferred embodiment of the presentinvention is a computer system (100) illustratively comprised of auser-interface personal computer (110) connected to anapplication-server personal computer (120) via a network (45). Theapplication server personal computer (120) is in turn connected via thenetwork (45) to a database-server personal computer (130). The userinterface personal computer (110) is also connected via the network (45)to an Internet browser appliance (90) that contains browser software(800) such as Microsoft Internet Explorer or Netscape Navigator.

[0073] The database-server personal computer (130) has a read/writerandom access memory (131), a hard drive (132) for storage of theapplication database (50), a keyboard (133), a communications bus (134),a display (135), a mouse (136), a CPU (137) and a printer (138).

[0074] The application-server personal computer (120) has a read/writerandom access memory (121), a hard drive (122) for storage of thenon-user-interface portion of the enterprise section of the applicationsoftware (200, 300 and 400) of the present invention, a keyboard (123),a communications bus (124), a display (125), a mouse (126), a CPU (127)and a printer (128). While only one client personal computer is shown inFIG. 3, it is to be understood that the application-server personalcomputer (120) can be networked to fifty or more client, user-interfacepersonal computers (110) via the network (45). The application-serverpersonal computer (120) can also be networked to fifty or more server,personal computers (130) via the network (45). It is to be understoodthat the diagram of FIG. 3 is merely illustrative of one embodiment ofthe present invention.

[0075] The user-interface personal computer (110) has a read/writerandom access memory (111), a hard drive (112) for storage of a clientdata-base (49) and the user-interface portion of the applicationsoftware (700), a keyboard (113), a communications bus (114), a display(115), a mouse (116), a CPU (117) and a printer (118).

[0076] The application software (200, 300 and 400) controls theperformance of the central processing unit (127) as it completes thecalculations required to support the production of the matrices of valueand risk for a commercial enterprise. In the embodiment illustratedherein, the application software program (200, 300, 400 and 500 iswritten in a combination of C++, Java and Visual Basic®. The applicationsoftware (200, 300, 400, and 500) can use Structured Query Language(SQL) for extracting data from the databases and the Internet (5, 10,12, 15, 17, 25, 30, 35, 37 and 40). The user (20) can optionallyinteract with the user-interface portion of the application software(700) using the browser software (800) in the browser appliance (90) toprovide information to the application software (200, 300, 400 and 500)for use in determining which data will be extracted and transferred tothe application database (50) by the data bots.

[0077] User input is initially saved to the client database (49) beforebeing transmitted to the communication bus (124) and on to the harddrive (122) of the application-server computer via the network (45).Following the program instructions of the application software, thecentral processing unit (127) accesses the extracted data and user inputby retrieving it from the hard drive (122) using the random accessmemory (121) as computation workspace in a manner that is well known.

[0078] The computers (110, 120, 130 and 139) shown in FIG. 3illustratively are IBM PCs or clones or any of the more powerfulcomputers or workstations that are widely available. Typical memoryconfigurations for client personal computers (110) used with the presentinvention should include at least 512 megabytes of semiconductor randomaccess memory (111) and at least a 100 gigabyte hard drive (112).Typical memory configurations for the application-server personalcomputer (120) used with the present invention should include at least2056 megabytes of semiconductor random access memory (121) and at leasta 250 gigabyte hard drive (122). Typical memory configurations for thedatabase-server personal computer (130) used with the present inventionshould include at least 4112 megabytes of semiconductor random accessmemory (131) and at least a 500 gigabyte hard drive (132).

[0079] Using the system described above the matrices of value and riskfor a multi-enterprise organization are produced after the elements ofvalue and external factors are analyzed by segment of value for eachenterprise in the organization using the approach outlined in Table 2.

[0080] As shown in Table 2, the value of the current-operation for eachenterprise will be calculated using an income valuation. An integralpart of most income valuation models is the calculation of the presentvalue of the expected cash flows, income or profits associated with thecurrent-operation. The present value of a stream of cash flows iscalculated by discounting the cash flows at a rate that reflects therisk associated with realizing the cash flow. For example, the presentvalue (PV) of a cash flow of ten dollars ($10) per year for five (5)years would vary depending on the rate used for discounting future cashflows as shown below. Discount rate = 25%${PV} = {{\frac{10}{1.25} + \frac{10}{(1.25)^{2}} + \frac{10}{(1.25)^{3}} + \frac{10}{(1.25)^{4}} + \frac{10}{(1.25)^{5}}} = 26.89}$

Discount rate = 35%${PV} = {{\frac{10}{1.35} + \frac{10}{(1.35)^{2}} + \frac{10}{(1.35)^{3}} + \frac{10}{(1.35)^{4}} + \frac{10}{(1.35)^{5}}} = 22.20}$

[0081] One of the first steps in evaluating the elements ofcurrent-operation value is extracting the data required to completecalculations in accordance with the formula that defines the value ofthe current-operation as shown in Table 4. TABLE 4 Value ofcurrent-operation = (R) Value of forecast revenue from current-operation(positive) + (E) Value of forecast expense for current-operation(negative) + (C)* Value of current operation capital change forecast

[0082] The three components of current-operation value will be referredto as the revenue value (R), the expense value (E) and the capital value(C). Examination of the equation in Table 4 shows that there are fourways to increase the value of the current-operation—increase therevenue, decrease the expense, decrease the capital requirements ordecrease the interest rate used for discounting future cash flows. As asimplification, the value of the current operation could be calculatedfrom the cash flow which is revenue (a positive number) plus expense (anegative number) and the change in capital (a positive or negativenumber). A slight adjustment to this basic equation would be required toremove the non-cash depreciation and amortization. The detailed analysisby component of value is utilized in the preferred embodiment.

[0083] In the preferred embodiment, the revenue, expense and capitalrequirement forecasts for the current operation, the real options andthe contingent liabilities are obtained from an advanced financialplanning system database (30) derived from an advanced financialplanning system similar to the one disclosed in U.S. Pat. No. 5,615,109.The extracted revenue, expense and capital requirement forecasts areused to calculate a cash flow for each period covered by the forecastfor the enterprise by subtracting the expense and change in capital foreach period from the revenue for each period. A steady state forecastfor future periods is calculated after determining the steady stategrowth rate that best fits the calculated cash flow for the forecasttime period. The steady state growth rate is used to calculate anextended cash flow forecast. The extended cash flow forecast is used todetermine the Competitive Advantage Period (CAP) implicit in theenterprise market value.

[0084] While it is possible to use analysis bots to sub-divide each ofthe components of current operation value into a number ofsub-components for analysis, the preferred embodiment has apre-determined number of sub-components for each component of value forthe enterprise. The revenue value is not subdivided. In the preferredembodiment, the expense value is subdivided into five sub-components:the cost of raw materials, the cost of manufacture or delivery ofservice, the cost of selling, the cost of support and the cost ofadministration. The capital value is subdivided into six sub-components:cash, non-cash financial assets, production equipment, other assets (nonfinancial, non production assets), financial liabilities and equity. Thecomponents and sub-components of current-operation value will be used invaluing the current operation portion of the elements and sub-elementsof value for each enterprise.

[0085] For the calculations completed by the present invention, atransaction will be defined as any event that is logged or recorded.Transaction data is any data related to a transaction. Descriptive datais any data related to any item, segment of value, element of value,component of value or external factor that is logged or recorded.Descriptive data includes forecast data and other data calculated by thesystem of the present invention. An element of value will be defined as“an entity or group that as a result of past transactions, forecasts orother data has provided and/or is expected to provide economic benefitto the enterprise.” An item will be defined as a single member of thegroup that defines an element of value. For example, an individualsalesman would be an “item” in the “element of value” sales staff. It ispossible to have only one item in an element of value. The transactiondata and descriptive data associated with an item or related group ofitems will be referred to as “item variables”. Data derived fromtransaction data and/or descriptive data are referred to as an itemperformance indicators. Composite variables for an element aremathematical or logical combinations of item variables and/or itemperformance indicators. The item variables, item performance indicatorsand composite variables for a specific element or sub-element of valuecan be referred to as element variables or element data. Externalfactors are numerical indicators of: conditions or prices external tothe enterprise and conditions or performance of the enterprise comparedto external expectations of conditions or performance. The transactiondata and descriptive data associated with external factors will bereferred to as “factor variables”. Data derived from factor transactiondata and/or descriptive data are referred to as factor performanceindicators. Composite factors for a factor are mathematical or logicalcombinations of factor variables and/or factor performance indicators.The factor variables, factor performance indicators and compositefactors for external factors can be referred to as factor data.

[0086] A value chain is defined to be the enterprises that have joinedtogether to deliver a product and/or a service to a customer. Consistentwith the practice outlined in the cross-referenced patents andapplications, an enterprise is a commercial enterprise with one revenuecomponent of value (note: as detailed in the related patents andapplications a commercial enterprise can have more than one revenuecomponent of value). A multi company corporation is a corporation thatparticipates in more than one distinct line of business. As discussedpreviously, value chains and multi company corporations are bothmulti-enterprise organizations. Partnerships between government agenciesand private companies and/or other government agencies can also beanalyzed as multi-enterprise organizations using the system of thepresent invention.

[0087] Analysis bots are used to determine element of value lives andthe percentage of: the revenue value, the expense value, and the capitalvalue that are attributable to each element of value by enterprise. Theresulting values are then added together to determine the valuation fordifferent elements as shown by the example in Table 5. TABLE 5 ElementGross Value Percentage Life/CAP* Net Value Revenue value = $120 M 20%80% Value = $19.2 M Expense value = ($80 M) 10% 80% Value = ($6.4) MCapital value = ($5 M)  5% 80% Value = ($0.2) M Total value = $35 M Netvalue for this element: Value = $12.6 M

[0088] The development of the matrices of value and risk for theorganization is completed in four distinct stages. As shown in FIG. 5A,FIG. 5B, FIG. 5C, FIG. 5D, FIG. 5E, FIG. 5F and FIG. 5G the first stageof processing (block 200 from FIG. 1) programs bots to continuallyextract, aggregate, manipulate and store the data from user input,databases and the Internet (5, 10, 12, 15, 17, 25, 30, 35, 37 and 40) asrequired for the analysis of business value and risk by enterprise. Botsare independent components of the application that have specific tasksto perform. As shown in FIG. 6A, FIG. 6B and FIG. 6C the second stage ofprocessing (block 300 from FIG. 1) continually values the segments ofvalue and generates a matrix quantifying the impact of elements of valueand external factors on the segments of value by enterprise (see FIG. 9)by creating and activating analysis bots to:

[0089] 1. Identify the factor variables, factor performance indicatorsand composite variables that characterize each external factors impacton: the current operation, derivative and excess financial assetsegments of value by enterprise,

[0090] 2. Identify the item variables, item performance indicators andcomposite variables for each element and sub-element of value thatcharacterize the elements performance in driving: the current operation,derivative and excess financial asset segments of value by enterprise,

[0091] 3. Create vectors that summarize the item variables, itemperformance indicators and composite variables that define the impact ofeach element of value and sub-element of value,

[0092] 4. Create vectors that summarize the factor variables, factorperformance indicators and composite variables that define the impact ofeach external factor,

[0093] 5. Determine the expected life of each element of value andsub-element of value;

[0094] 6. Determine the value of the current operation, excess financialassets and derivatives;

[0095] 7. Determine the appropriate discount rate on the basis ofrelative causal element strength, value the enterprise real options andcontingent liabilities and determine the contribution of each element ofvalue to real option valuation;

[0096] 8. Determine the best indicator for stock price movement,calculate market sentiment and analyze the causes of market sentiment;

[0097] 9. Combine the results of the prior stages of processing todetermine the value of each external factor, element of value andsub-element of value by segment for each enterprise; and

[0098] 10. Sum the results from all the enterprises to calculate theoverall organization value.

[0099] The third stage of processing (block 400 from FIG. 1) analyzesthe risks faced by each enterprise under normal and extreme conditionsas required to develop the matrix of risk (see FIG. 10) for theorganization before defining the efficient frontier for financialperformance. The fourth and final stage of processing (block 500 fromFIG. 1) displays the matrix of value, the matrix of risk and theefficient frontier for the organization and analyzes the impact ofchanges in structure and/or operation on the financial performance ofthe multi-enterprise organization.

[0100] System Settings and Data Bots

[0101] The flow diagrams in FIG. 5A, FIG. 5B, FIG. 5C, FIG. 5D, FIG. 5E,FIG. 5F and FIG. 5G detail the processing that is completed by theportion of the application software (200) that extracts, aggregates,transforms and stores the information required for system operation fromthe: basic financial system database (5), operation management systemdatabase (10), the web site transaction log database (12), humanresource information system database (15), risk management systemdatabase (17), external database (25), advanced financial systemdatabase (30), asset management system database (35), the supply chainsystem database (37), the Internet (40) and the user (20) by enterprise.A brief overview of the different databases will be presented beforereviewing each step of processing completed by this portion (200) of theapplication software.

[0102] Corporate financial software systems are generally divided intotwo categories, basic and advanced. Advanced financial systems utilizeinformation from the basic financial systems to perform financialanalysis, financial planning and financial reporting functions.Virtually every commercial enterprise uses some type of basic financialsystem, as they are required to use these systems to maintain books andrecords for income tax purposes. An increasingly large percentage ofthese basic financial systems are resident in microcomputer andworkstation systems. Basic financial systems include general-ledgeraccounting systems with associated accounts receivable, accountspayable, capital asset, inventory, invoicing, payroll and purchasingsubsystems. These systems incorporate worksheets, files, tables anddatabases. These databases, tables and files contain information aboutthe enterprise operations and its related accounting transactions. Aswill be detailed below, these databases, tables and files are accessedby the application software of the present invention as required toextract the information required for completing a business valuation.The system is also capable of extracting the required information from adata warehouse (or datamart) when the required information has beenpre-loaded into the warehouse.

[0103] General ledger accounting systems generally store only validaccounting transactions. As is well known, valid accounting transactionsconsist of a debit component and a credit component where the absolutevalue of the debit component is equal to the absolute value of thecredit component. The debits and the credits are posted to the separateaccounts maintained within the accounting system. Every basic accountingsystem has several different types of accounts. The effect that theposted debits and credits have on the different accounts depends on theaccount type as shown in Table 6. TABLE 6 Account Type: Debit Impact:Credit Impact: Asset Increase Decrease Revenue Decrease Increase ExpenseIncrease Decrease Liability Decrease Increase Equity Decrease Increase

[0104] General ledger accounting systems also require that the assetaccount balances equal the sum of the liability account balances andequity account balances at all times.

[0105] The general ledger system generally maintains summary, dollaronly transaction histories and balances for all accounts while theassociated subsystems, accounts payable, accounts receivable, inventory,invoicing, payroll and purchasing, maintain more detailed historicaltransaction data and balances for their respective accounts. It iscommon practice for each subsystem to maintain the detailed informationshown in Table 7 for each transaction. TABLE 7 Subsystem DetailedInformation Accounts Vendor, Item(s), Transaction Date, Amount PayableOwed, Due Date, Account Number Accounts Customer, Transaction Date,Product Sold, Receivable Quantity, Price, Amount Due, Terms, Due Date,Account Number Capital Asset ID, Asset Type, Date of Purchase, AssetsPurchase Price, Useful Life, Depreciation Schedule, Salvage ValueInventory Item Number, Transaction Date, Transaction Type, TransactionQty, Location, Account Number Invoicing Customer Name, Transaction Date,Product(s) Sold, Amount Due, Due Date, Account Number Payroll EmployeeName, Employee Title, Pay Frequency, Pay Rate, Account Number PurchasingVendor, Item(s), Purchase Quantity, Purchase Price(s), Due Date, AccountNumber

[0106] As is well known, the output from a general ledger systemincludes income statements, balance sheets and cash flow statements inwell defined formats which assist management in measuring the financialperformance of the firm during the prior periods when data input andsystem processing have been completed.

[0107] While basic financial systems are similar between firms,operation management systems vary widely depending on the type ofcompany they are supporting. These systems typically have the ability tonot only track historical transactions but to forecast futureperformance. For manufacturing firms, operation management systems suchas Enterprise Resource Planning Systems (ERP), Material RequirementPlanning Systems (MRP), Purchasing Systems, Scheduling Systems andQuality Control Systems are used to monitor, coordinate, track and planthe transformation of materials and labor into products. Systems similarto the one described above may also be useful for distributors to use inmonitoring the flow of products from a manufacturer.

[0108] Operation Management Systems in manufacturing firms may alsomonitor information relating to the production rates and the performanceof individual production workers, production lines, work centers,production teams and pieces of production equipment including theinformation shown in Table 8. TABLE 8 Operation Management System -Production Information 1. ID number (employee id/machine id) 2. Actualhours - last batch 3. Standard hours - last batch 4. Actual hours - yearto date 5. Actual/Standard hours - year to date % 6. Actual setup time -last batch 7. Standard setup time - last batch 8. Actual setup hours -year to date 9. Actual/Standard setup hrs - yr to date % 10. Cumulativetraining time 11. Job(s) certifications 12. Actual scrap - last batch13. Scrap allowance - last batch 14. Actual scrap/allowance - year todate 15. Rework time/unit last batch 16. Rework time/unit year to date17. QC rejection rate - batch 18. QC rejection rate - year to date

[0109] Operation management systems are also useful for trackingrequests for service to repair equipment in the field or in acentralized repair facility. Such systems generally store informationsimilar to that shown below in Table 9. TABLE 9 Operation ManagementSystem - Service Call Information 1. Customer name 2. Customer number 3.Contract number 4. Service call number 5. Time call received 6.Product(s) being fixed 7. Serial number of equipment 8. Name of personplacing call 9. Name of person accepting call 10. Promised response time11. Promised type of response 12. Time person dispatched to call 13.Name of person handling call 14. Time of arrival on site 15. Time ofrepair completion 16. Actual response type 17. Part(s) replaced 18.Part(s) repaired 19. 2nd call required 20. 2nd call number

[0110] Web site transaction log databases keep a detailed record ofevery visit to a web site, they can be used to trace the path of eachvisitor to the web site and upon further analysis can be used toidentify patterns that are most likely to result in purchases and thosethat are most likely to result in abandonment. This information can alsobe used to identify which promotion would generate the most value forthe enterprise using the system. Web site transaction logs generallycontain the information shown in Table 10. TABLE 10 Web Site TransactionLog Database 1. Customer's URL 2. Date and time of visit 3. Pagesvisited 4. Length of page visit (time) 5. Type of browser used 6.Referring site 7. URL of site visited next 8. Downloaded file volume andtype 9. Cookies 10. Transactions

[0111] Computer based human resource systems may some times be packagedor bundled within enterprise resource planning systems such as thoseavailable from SAP, Oracle and Peoplesoft. Human resource systems areincreasingly used for storing and maintaining corporate recordsconcerning active employees in sales, operations and the otherfunctional specialties that exist within a modern corporation. Storingrecords in a centralized system facilitates timely, accurate reportingof overall manpower statistics to the corporate management groups andthe various government agencies that require periodic updates. In somecases, human resource systems include the enterprise payroll system as asubsystem. In the preferred embodiment of the present invention, thepayroll system is part of the basic financial system. These systems canalso be used for detailed planning regarding future manpowerrequirements. Human resource systems typically incorporate worksheets,files, tables and databases that contain information about the currentand future employees. As will be detailed below, these databases, tablesand files are accessed by the application software of the presentinvention as required to extract the information required for completinga business valuation. It is common practice for human resource systemsto store the information shown in Table 11 for each employee. TABLE 11Human Resource System Information 1. Employee name 2. Job title 3. Jobcode 4. Rating 5. Division 6. Department 7. Employee No./(SocialSecurity Number) 8. Year to date - hours paid 9. Year to date - hoursworked 10. Employee start date - enterprise 11. Employee start date -department 12. Employee start date - current job 13. Training coursescompleted 14. Cumulative training expenditures 15. Salary history 16.Current salary 17. Educational background 18. Current supervisor

[0112] Risk management systems databases (17) contain statistical dataabout the past behavior and forecasts of likely future behavior ofinterest rates, currency exchange rates weather, commodity prices andkey customers (credit risk systems). They also contain detailedinformation about the composition and mix of risk reduction products(derivatives, insurance, etc.) the enterprise has purchased. Somecompanies also use risk management systems to evaluate the desirabilityof extending or increasing credit lines to customers. The informationfrom these systems is used to supplement the risk information developedby the system of the present invention.

[0113] External databases can be used for obtaining information thatenables the definition and evaluation of a variety of things includingelements of value, external factors, industry real options and eventrisks. In some cases, information from these databases can be used tosupplement information obtained from the other databases and theInternet (5, 10, 12, 15, 17, 30, 35, 37 and 40). In the system of thepresent invention, the information extracted from external databases(25) can be in the forms listed in Table 12. TABLE 12 Types ofinformation 1) numeric information such as that found in the SEC Edgardatabase and the databases of financial infomediaries such as FirstCall,IBES and Compustat, 2) text information such as that found in the LexisNexis database and databases containing past issues from specificpublications, 3) risk management products such as derivatives, swaps andstandardized insurance contracts that can be purchased on line, 4)geospatial data; 5) multimedia information such as video and audioclips, and 6) event risk data including information about the likelihoodof earthquake and weather damage by geospatial location and informationabout the likelihood of property and casualty losses that can bedetermined in part by the industry the enterprise is a member of (i.e.coal mining, broadcasting, legal, etc.)

[0114] The system of the present invention uses different “bot” types toprocess each distinct data type from external databases (25). The same“bot types” are also used for extracting each of the different types ofdata from the Internet (40). The system of the present invention musthave access to at least one external database (25) that providesinformation regarding the equity prices for each enterprise and theequity prices and financial performance of the competitors for eachenterprise.

[0115] Advanced financial systems may also use information from externaldatabases (25) and the Internet (40) in completing their processing.Advanced financial systems include financial planning systems andactivity based costing systems. Activity based costing systems may beused to supplement or displace the operation of the expense componentanalysis segment of the present invention. Financial planning systemsgenerally use the same format used by basic financial systems inforecasting income statements, balance sheets and cash flow statementsfor future periods. Management uses the output from financial planningsystems to highlight future financial difficulties with a lead timesufficient to permit effective corrective action and to identifyproblems in enterprise operations that may be reducing the profitabilityof the business below desired levels. These systems are most oftendeveloped by individuals within companies using two andthree-dimensional spreadsheets such as Lotus 1-2-3®, Microsoft Excel®and Quattro Pro®. In some cases, financial planning systems are builtwithin an executive information system (EIS) or decision support system(DSS). For the preferred embodiment of the present invention, theadvanced finance system database is similar to the financial planningsystem database detailed in U.S. Pat. No. 5,165,109 for “Method of andSystem for Generating Feasible, Profit Maximizing Requisition Sets”, byJeff S. Eder, the disclosure of which is incorporated herein byreference.

[0116] While advanced financial planning systems have been around forsome time, asset management systems are a relatively recent development.Their appearance is further proof of the increasing importance of “soft”assets. Asset management systems include: customer relationshipmanagement systems, partner relationship management systems, channelmanagement systems, knowledge management systems, visitor relationshipmanagement systems, intellectual property management systems, investormanagement systems, vendor management systems, alliance managementsystems, process management systems, brand management systems, workforcemanagement systems, human resource management systems, email managementsystems, IT management systems and/or quality management systems. Assetmanagement systems are similar to operation management systems in thatthey generally have the ability to forecast future events as well astrack historical occurrences. Many have also added analyticalcapabilities that allow them to identify trends and patterns in the dataassociated with the asset they are managing. Customer relationshipmanagement systems are the most well established asset managementsystems at this point and will be the focus of the discussion regardingasset management system data. In firms that sell customized products,the customer relationship management system is generally integrated withan estimating system that tracks the flow of estimates into quotations,orders and eventually bills of lading and invoices. In other firms thatsell more standardized products, customer relationship managementsystems generally are used to track the sales process from leadgeneration to lead qualification to sales call to proposal to acceptance(or rejection) and delivery. All customer relationship managementsystems would be expected to track all of the customer's interactionswith the enterprise after the first sale and store information similarto that shown below in Table 13. TABLE 13 Customer RelationshipManagement System - Information 1. Customer/Potential customer name 2.Customer number 3. Address 4. Phone number 5. Source of lead 6. Date offirst purchase 7. Date of last purchase 8. Last sales call/contact 9.Sales call history 10. Sales contact history 11. Sales history:product/qty/price 12. Quotations: product/qty/price 13. Custom productpercentage 14. Payment history 15. Current A/R balance 16. Average daysto pay

[0117] Supply chain systems could be considered as asset managementsystems as they are used to manage a critical asset—supplierrelationships. However, because of their importance and visibility theyare listed separately. Supply chain management system databases (37)contain information that may have been in operation management systemdatabases (10) in the past. These systems provide enhanced visibilityinto the availability of goods and promote improved coordination betweencustomers and their suppliers. All supply chain management systems wouldbe expected to track all of the items ordered by the enterprise afterthe first purchase and store information similar to that shown below inTable 14. TABLE 14 Supply Chain Management System Information 1. StockKeeping Unit (SKU) 2. Vendor 3. Total Quantity on Order 4. TotalQuantity in Transit 5. Total Quantity on Back Order 6. Total Quantity inInventory 7. Quantity available today 8. Quantity available next 7 days9. Quantity available next 30 days 10. Quantity available next 90 days11. Quoted lead time 12. Actual average lead time

[0118] System processing of the information from the different databases(5, 10, 12, 15, 17, 25, 30, 35 and 37) and the Internet (40) describedabove starts in a block 201, FIG. 5A, which immediately passesprocessing to a software block 202. The software in block 202 promptsthe user (20) via the system settings data window (701) to providesystem setting information. The system setting information entered bythe user (20) is transmitted via the network (45) back to theapplication server (120) where it is stored in the system settings table(140) in the application database (50) in a manner that is well known.The specific inputs the user (20) is asked to provide at this point inprocessing are shown in Table 15. TABLE 15 1. New calculation orstructure revision? 2. Continuous, If yes, new calculation frequency?(hourly, daily, weekly, monthly or quarterly) 3. Structure oforganization (enterprises) 4. Structure of enterprise (segments ofvalue, elements of value etc.) 5. Enterprise checklist 6. Base accountstructure 7. Base currency 8. Location of account structure 9. Metadatastandard 10. Location of basic financial system database and metadata byenterprise 11. Location of advanced finance system database and metadataby enterprise 12. Location of human resource information system databaseand metadata by enterprise 13. Location of operation management systemdatabase and metadata by enterprise 14. Location of asset managementsystem databases and metadata by enterprise 15. Location of externaldatabases and metadata by enterprise 16. Location of web sitetransaction log database and metadata by enterprise 17. Location ofsupply chain management system database and metadata by enterprise 18.Location of risk management system database and metadata by enterprise19. Location of database and metadata for equity information byenterprise 20. Location of database and metadata for debt information byenterprise 21. Location of database and metadata for tax rateinformation by enterprise 22. Location of database and metadata forcurrency conversion rate information 23. Geospatial data? If yes,identity of geocoding service. 24. The maximum number of generations tobe processed without improving fitness 25. Default clustering algorithm(selected from list) and maximum cluster number 26. Minimum amount ofcash and marketable securities required for operations 27. Total cost ofcapital (weighted average cost of equity, debt and risk capital) 28.Number of months a product is considered new after it is first produced29. Enterprise industry classification (SIC Code) 30. Primarycompetitors by industry classification by enterprise 31. Managementreport types (text, graphic, both) 32. Default Missing Data Procedure33. Maximum time to wait for user input 34. Maximum discount rate fornew projects (real option valuation) 35. Detailed valuation usingcomponents of current operation value? (yes or no) 36. Use of industryreal options? (yes or no) 37. Maximum number of sub-elements 38.Confidence interval for risk reduction programs 39. Minimum workingcapital level (optional) 40. Semantic mapping? (yes or no)

[0119] The enterprise checklist data are used by a “rules” engine (suchas the one available from Neuron Data) in block 202 to influence thenumber and type of items with pre-defined metadata mapping for eachcategory of value. For example, if the checklist data indicates that theenterprise is focused on branded, consumer markets, then additionalbrand related factors would be pre-defined for mapping. The applicationof these system settings will be further explained as part of thedetailed explanation of the system operation.

[0120] The software in block 202 uses the current system date todetermine the time periods (months) that require data to complete thecalculations. After the date range is calculated it is stored in thesystem settings table (140). In the preferred embodiment the analysis ofenterprise value and risk by the system obtains and utilizes data fromevery source for the four year period before and the three year forecastperiod after the specified valuation date and/or the date of systemcalculation. The user (20) also has the option of specifying the dataperiods that will be used for completing system calculations. After thestorage of system setting data is complete, processing advances to asoftware block 203. The software in block 203 prompts the user (20) viathe metadata and conversion rules window (702) to map metadata using thestandard specified by the user (20) from the basic financial systemdatabase (5), the operation management system database (10), the website transaction log database (12), the human resource informationsystem database (15), the risk management system database (17), theexternal database (25), the advanced financial system database (30), theasset management system database (35) and the supply chain systemdatabase (37) to the enterprise hierarchy stored in the system settingstable (140) and to the pre-specified fields in the metadata mappingtable (141). Pre-specified fields in the metadata mapping table includethe revenue, expense and capital components and sub-components ofcurrent operation value for the enterprise and pre-specified fields forexpected value drivers by element of value and external factor. Becausethe bulk of the information being extracted is financial information,the metadata mapping often takes the form of specifying the accountnumber ranges that correspond to the different fields in the metadatamapping table (141). Table 16 shows the base account number structurethat the account numbers in the other systems must align with. Forexample, using the structure shown below, the revenue component for theenterprise could be specified as enterprise 01, any department number,accounts 400 to 499 (the revenue account range) with any sub-account.TABLE 16 Account Number 01 - 902 (any) - 477- 86 (any) SectionEnterprise Department Account Sub-account Subgroup Workstation MarketingRevenue Singapore Position 4 3 2 1

[0121] As part of the metadata mapping process, any database fields thatare not mapped to pre-specified fields are defined by the user (20) ascomponent of value, elements of value or non-relevant attributes and“mapped” in the metadata mapping table (141) to the corresponding fieldsin each database in a manner identical to that described above for thepre-specified fields. After all fields have been mapped to the metadatamapping table (141), the software in block 203 prompts the user (20) viathe metadata and conversion rules window (702) to provide conversionrules for each metadata field for each data source. Conversion ruleswill include information regarding currency conversions and conversionfor units of measure that may be required to accurately and consistentlyanalyze the data. The inputs from the user (20) regarding conversionrules are stored in the conversion rules table (142) in the applicationdatabase (50). When conversion rules have been stored for all fieldsfrom every data source, then processing advances to a software block204.

[0122] The software in block 204 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. The calculation (or run) maybe new because the system is running for first time or it may be becausethe system is running continuously and it is now time for a newcalculation to be completed. If the calculation is not a new calculationor a structure change then processing advances to a software block 212.Alternatively, if the calculation is new or a structure change, thenprocessing advances to a software block 207.

[0123] The software in block 207 checks the bot date table (149) anddeactivates any basic financial system data bots with creation datesbefore the current system date and retrieves information from the systemsettings table (140), metadata mapping table (141) and conversion rulestable (142). The software in block 207 then initializes data bots foreach field in the metadata mapping table (141) that mapped to the basicfinancial system database (5) in accordance with the frequency specifiedby user (20) in the system settings table (140). Bots are independentcomponents of the application that have specific tasks to perform. Inthe case of data acquisition bots, their tasks are to extract andconvert transaction and descriptive data from a specified source andthen store it in a specified location. Each data bot initialized bysoftware block 207 will store its data in the basic financial systemtable (143) and/or the derivatives table (175). Every data acquisitionbot contains the information shown in Table 17. TABLE 17 1. Unique IDnumber (based on date, hour, minute, second of creation) 2. The datasource location 3. Mapping information 4. Timing of extraction 5.Conversion rules (if any) 6. Storage Location (to allow for tracking ofsource and destination events) 7. Organization 8. Enterprise 9. Creationdate (date, hour, minute, second)

[0124] After the software in block 207 initializes all the bots for thebasic financial system database, processing advances to a block 208. Inblock 208, the bots extract and convert transaction and descriptive datafrom the basic financial system (5) in accordance with theirpreprogrammed instructions in accordance with the frequency specified byuser (20) in the system settings table (140). As each bot extracts andconverts data from the basic financial system database (5) byenterprise, processing advances to a software block 209 before the botcompletes data storage. The software in block 209 checks the basicfinancial system metadata to see if all fields have been extracted. Ifthe software in block 209 finds no unmapped data fields, then theextracted, converted data are stored in the basic financial system table(143) by enterprise. Alternatively, if there are fields that have notbeen extracted, then processing advances to a block 211. The software inblock 211 prompts the user (20) via the metadata and conversion ruleswindow (702) to provide metadata and conversion rules for each newfield. The information regarding the new metadata and conversion rulesis stored in the metadata mapping table (141) and conversion rules table(142) while the extracted, converted data are stored in the basicfinancial system table (143) by enterprise. It is worth noting at thispoint that the activation and operation of bots where all the fieldshave been mapped to the application database (50) continues. Only botswith unmapped fields “wait” for user input before completing datastorage. The new metadata and conversion rule information will be usedthe next time bots are initialized in accordance with the frequencyestablished by the user (20). In either event, system processing passeson to software block 212.

[0125] The software in block 212 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change then processing advances to asoftware block 228. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 221.

[0126] The software in block 221 checks the bot date table (149) anddeactivates any operation management system data bots with creationdates before the current system date and retrieves information from thesystem settings table (140), metadata mapping table (141) and conversionrules table (142). The software in block 221 then initializes data botsfor each field in the metadata mapping table (141) that mapped to theoperation management system database (10) in accordance with thefrequency specified by user (20) in the system settings table (140).Each data bot initialized by software block 221 will store its data inthe operation system table (144) by enterprise.

[0127] After the software in block 221 initializes all the bots for theoperation management system database, processing advances to a block222. In block 222, the bots extract and convert transaction anddescriptive data from the operation management system database (10) inaccordance with their preprogrammed instructions in accordance with thefrequency specified by user (20) in the system settings table (140). Aseach bot extracts and converts data from the operation management systemdatabase (10), processing advances to a software block 209 before thebot completes data storage. The software in block 209 checks theoperation management system metadata to see if all fields have beenextracted. If the software in block 209 finds no unmapped data fields,then the extracted, converted data are stored in the operation systemtable (144) by enterprise. Alternatively, if there are fields that havenot been extracted, then processing advances to a block 211. Thesoftware in block 211 prompts the user (20) via the by metadata andconversion rules window (702) to provide metadata and conversion rulesfor each new field. The information regarding the new metadata andconversion rules is stored in the metadata mapping table (141) andconversion rules table (142) while the extracted, converted data arestored in the operation system table (144) by enterprise. It is worthnoting at this point that the activation and operation of bots where allthe fields have been mapped to the application database (50) continues.Only bots with unmapped fields “wait” for user input before completingdata storage. The new metadata and conversion rule information will beused the next time bots are initialized in accordance with the frequencyestablished by the user (20). In either event, system processing thenpasses on to a software block 225.

[0128] The software in block 225 checks the bot date table (149) anddeactivates any web site transaction log data bots with creation datesbefore the current system date and retrieves information from the systemsettings table (140), metadata mapping table (141) and conversion rulestable (142). The software in block 225 then initializes data bots foreach field in the metadata mapping table (141) that mapped to the website transaction log database (12) by enterprise in accordance with thefrequency specified by user (20) in the system settings table (140).Each data bot initialized by software block 225 will store its data inthe web log data table (172) by enterprise.

[0129] After the software in block 225 initializes all the bots for theweb site transaction log database, the bots extract and converttransaction and descriptive data in accordance with their preprogrammedinstructions in accordance with the frequency specified by user (20) inthe system settings table (140). As each bot extracts and converts datafrom the web site transaction log database (12), processing advances toa software block 209 before the bot completes data storage. The softwarein block 209 checks the web site transaction log metadata to see if allfields have been extracted. If the software in block 209 finds nounmapped data fields, then the extracted, converted data are stored inthe web log data table (172) by enterprise. Alternatively, if there arefields that have not been extracted, then processing advances to a block211. The software in block 211 prompts the user (20) via the metadataand conversion rules window (702) to provide metadata and conversionrules for each new field. The information regarding the new metadata andconversion rules is stored in the metadata mapping table (141) andconversion rules table (142) while the extracted, converted data arestored in the web log data table (172) by enterprise. It is worth notingat this point that the activation and operation of bots where all thefields have been mapped to the application database (50) continues. Onlybots with unmapped fields “wait” for user input before completing datastorage. The new metadata and conversion rule information will be usedthe next time bots are initialized in accordance with the frequencyestablished by the user (20). In either event, system processing thenpasses on to a software block 226.

[0130] The software in block 226 checks the bot date table (149) anddeactivates any human resource information system data bots withcreation dates before the current system date and retrieves informationfrom the system settings table (140), metadata mapping table (141) andconversion rules table (142). The software in block 226 then initializesdata bots for each field in the metadata mapping table (141) that mappedto the human resource information system database (15) in accordancewith the frequency specified by user (20) in the system settings table(140). Each data bot initialized by software block 226 will store itsdata in the human resource system table (145) by enterprise.

[0131] After the software in block 226 initializes all the bots for thehuman resource information system database, the bots extract and converttransaction and descriptive data in accordance with their preprogrammedinstructions in accordance with the frequency specified by user (20) inthe system settings table (140) by enterprise. As each bot extracts andconverts data from the human resource information system database (15),processing advances to a software block 209 before the bot completesdata storage. The software in block 209 checks the human resourceinformation system metadata to see if all fields have been extracted. Ifthe software in block 209 finds no unmapped data fields, then theextracted, converted data are stored in the human resource system table(145) by enterprise. Alternatively, if there are fields that haven'tbeen extracted, then processing advances to a block 211. The software inblock 211 prompts the user (20) via the metadata and conversion ruleswindow (702) to provide metadata and conversion rules for each newfield. The information regarding the new metadata and conversion rulesis stored in the metadata mapping table (141) and conversion rules table(142) while the extracted, converted data are stored in the humanresource system table (145) by enterprise. It is worth noting at thispoint that the activation and operation of bots where all the fieldshave been mapped to the application database (50) continues. Only botswith unmapped fields “wait” for user input before completing datastorage. The new metadata and conversion rule information will be usedthe next time bots are initialized in accordance with the frequencyestablished by the user (20). In either event, system processing thenpasses on to software block 228.

[0132] The software in block 228 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change then processing advances to asoftware block 248. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 241.

[0133] The software in block 241 checks the bot date table (149) anddeactivates any external database data bots with creation dates beforethe current system date and retrieves information from the systemsettings table (140), metadata mapping table (141) and conversion rulestable (142). The software in block 241 then initializes data bots foreach field in the metadata mapping table (141) that mapped to theexternal database (25) in accordance with the frequency specified byuser (20) in the system settings table (140). Each data bot initializedby software block 241 will store its data in the external database table(146) by enterprise.

[0134] After the software in block 241 initializes all the bots for theexternal database, processing advances to a block 242. In block 242, thebots extract, convert and assign transaction and descriptive data inaccordance with their preprogrammed instructions. As each bot extracts,converts and assigns data from the external database (25), processingadvances to a software block 209 before the bot completes data storageand assignments. The software in block 209 checks the external databasemetadata to see if the extracted data are assigned to specified fields.If the software in block 209 finds no unmapped data, then the extracted,converted data are stored in the external database table (146) byenterprise. Alternatively, if there are fields that do not have metadataassignments, then processing advances to a block 211. The software inblock 211 prompts the user (20) via the metadata and conversion ruleswindow (702) to provide metadata, conversion rules and assignments foreach new field. The information regarding the new metadata andconversion rules is stored in the metadata mapping table (141) andconversion rules table (142) while the information regarding the newassignments is stored in the external factor definition table (169).While some external factors are pre-defined for analysis, the bulk ofthe external factors are not pre-assigned and are developed usingavailable data that is assigned to an external factor at the time ofextraction. The extracted, converted data with new assignments is thenstored in the external database table (146) by enterprise. It is worthnoting at this point that the activation and operation of bots where allthe fields have been mapped to the application database (50) continues.Only bots with unmapped fields “wait” for user input before completingdata storage. The new metadata, conversion rule and classificationinformation will be used the next time bots are initialized inaccordance with the frequency established by the user (20). In eitherevent, system processing then passes on to a software block 245.

[0135] The software in block 245 checks the bot date table (149) anddeactivates any advanced financial system data bots with creation datesbefore the current system date and retrieves information from the systemsettings table (140), metadata mapping table (141) and conversion rulestable (142). The software in block 245 then initializes data bots foreach field in the metadata mapping table (141) that mapped to theadvanced financial system database (30) in accordance with the frequencyspecified by user (20) in the system settings table (140). Each data botinitialized by software block 245 will store its data in the advancedfinance system database table (147) by enterprise.

[0136] After the software in block 245 initializes all the bots for theadvanced finance system database, the bots extract and converttransaction and descriptive data in accordance with their preprogrammedinstructions in accordance with the frequency specified by user (20) inthe system settings table (140). As each bot extracts and converts datafrom the advanced financial system database (30) by enterprise,processing advances to a software block 209 before the bot completesdata storage. The software in block 209 checks the advanced financesystem database metadata to see if all fields have been extracted. Ifthe software in block 209 finds no unmapped data fields, then theextracted, converted data are stored in the advanced finance systemdatabase table (147) by enterprise. Alternatively, if there are fieldsthat haven't been extracted, then processing advances to a block 211.The software in block 211 prompts the user (20) via the metadata andconversion rules window (702) to provide metadata and conversion rulesfor each new field. The information regarding the new metadata andconversion rules is stored in the metadata mapping table (141) andconversion rules table (142) while the extracted, converted data arestored in the advanced finance system database table (147) byenterprise. It is worth noting at this point that the activation andoperation of bots where all the fields have been mapped to theapplication database (50) continues. Only bots with unmapped fields“wait” for user input before completing data storage. The new metadataand conversion rule information will be used the next time bots areinitialized in accordance with the frequency established by the user(20). In either event, system processing then passes on to softwareblock 246.

[0137] The software in block 246 checks the bot date table (149) anddeactivates any asset management system data bots with creation datesbefore the current system date and retrieves information from the systemsettings table (140), metadata mapping table (141) and conversion rulestable (142). The software in block 246 then initializes data bots foreach field in the metadata mapping table (141) that mapped to a assetmanagement system database (35) in accordance with the frequencyspecified by user (20) in the system settings table (140). Extractingdata from each asset management system ensures that the management ofeach soft asset is considered and prioritized within the overallfinancial models for the enterprise. Each data bot initialized bysoftware block 246 will store its data in the asset system table (148)by enterprise.

[0138] After the software in block 246 initializes bots for all assetmanagement system databases, the bots extract and convert transactionand descriptive data in accordance with their preprogrammed instructionsin accordance with the frequency specified by user (20) in the systemsettings table (140). As each bot extracts and converts data from theasset management system databases (35), processing advances to asoftware block 209 before the bot completes data storage. The softwarein block 209 checks the metadata for the asset management systemdatabases to see if all fields have been extracted. If the software inblock 209 finds no unmapped data fields, then the extracted, converteddata are stored in the asset system table (148) by enterprise.Alternatively, if there are fields that haven't been extracted, thenprocessing advances to a block 211. The software in block 211 promptsthe user (20) via the metadata and conversion rules window (702) toprovide metadata and conversion rules for each new field. Theinformation regarding the new metadata and conversion rules is stored inthe metadata mapping table (141) and conversion rules table (142) whilethe extracted, converted data are stored in the asset system table (148)by enterprise. It is worth noting at this point that the activation andoperation of bots where all the fields have been mapped to theapplication database (50) continues. Only bots with unmapped fields“wait” for user input before completing data storage. The new metadataand conversion rule information will be used the next time bots areinitialized in accordance with the frequency established by the user(20). In either event, system processing then passes on to softwareblock 248.

[0139] The software in block 248 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change then processing advances to asoftware block 254. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 251.

[0140] The software in block 251 checks the bot date table (149) anddeactivates any risk management system data bots with creation datesbefore the current system date and retrieves information from the systemsettings table (140), metadata mapping table (141) and conversion rulestable (142). The software in block 251 then initializes data bots foreach field in the metadata mapping table (141) that mapped to a riskmanagement system database (17) in accordance with the frequencyspecified by user (20) in the system settings table (140). Each data botinitialized by software block 251 will store its data in the risk systemtable (176) and/or the derivatives table (175) by enterprise.

[0141] After the software in block 251 initializes bots for all riskmanagement system databases for each enterprise, the bots extract andconvert transaction and descriptive data in accordance with theirpreprogrammed instructions in accordance with the frequency specified byuser (20) in the system settings table (140) by enterprise. As each botextracts and converts data from the risk management system databases(17), processing advances to a software block 209 before the botcompletes data storage. The software in block 209 checks the metadatafor the risk management system database (17) to see if all fields havebeen extracted. If the software in block 209 finds no unmapped datafields, then the extracted, converted data are stored in the risk systemtable (176) and/or the derivatives table (175) by enterprise.Alternatively, if there are fields that have not been extracted, thenprocessing advances to a block 211. The software in block 211 promptsthe user (20) via the metadata and conversion rules window (702) toprovide metadata and conversion rules for each new field. Theinformation regarding the new metadata and conversion rules is stored inthe metadata mapping table (141) and conversion rules table (142) whilethe extracted, converted data are stored in the risk management systemtable (174) and/or the derivatives table (175) by enterprise. It isworth noting at this point that the activation and operation of botswhere all the fields have been mapped to the application database (50)continues. Only bots with unmapped fields “wait” for user input beforecompleting data storage. The new metadata and conversion ruleinformation will be used the next time bots are initialized inaccordance with the frequency established by the user (20). In eitherevent, system processing then passes on to software block 252.

[0142] The software in block 252 checks the bot date table (149) anddeactivates any supply chain system data bots with creation dates beforethe current system date and retrieves information from the systemsettings table (140), metadata mapping table (141) and conversion rulestable (142). The software in block 252 then initializes data bots foreach field in the metadata mapping table (141) that mapped to a supplychain system database (37) in accordance with the frequency specified byuser (20) in the system settings table (140). Each data bot initializedby software block 252 will store its data in the supply chain systemtable (174) by enterprise.

[0143] After the software in block 252 initializes bots for all supplychain system databases, the bots extract and convert transaction anddescriptive data in accordance with their preprogrammed instructions inaccordance with the frequency specified by user (20) in the systemsettings table (140). As each bot extracts and converts data from thesupply chain system databases (37), processing advances to a softwareblock 209 before the bot completes data storage. The software in block209 checks the metadata for the supply chain system database (37) to seeif all fields have been extracted. If the software in block 209 finds nounmapped data fields, then the extracted, converted data are stored inthe supply chain system table (174) by enterprise. Alternatively, ifthere are fields that have not been extracted, then processing advancesto a block 211. The software in block 211 prompts the user (20) via themetadata and conversion rules window (702) to provide metadata andconversion rules for each new field. The information regarding the newmetadata and conversion rules is stored in the metadata mapping table(141) and conversion rules table (142) while the extracted, converteddata are stored in the supply chain system table (174) by enterprise. Itis worth noting at this point that the activation and operation of botswhere all the fields have been mapped to the application database (50)continues. Only bots with unmapped fields “wait” for user input beforecompleting data storage. The new metadata and conversion ruleinformation will be used the next time bots are initialized inaccordance with the frequency established by the user (20). In eitherevent, system processing then passes on to software block 254.

[0144] The software in block 254 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change then processing advances to asoftware block 276. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 255.

[0145] The software in block 255 prompts the user (20) via theidentification and classification rules window (703) to identifykeywords such as company names, brands, trademarks, competitors, risksand trends for pre-specified fields in the metadata mapping table (141)by enterprise. After specifying the keywords, the user (20) is promptedto classify each keyword by element, factor, enterprise or industry(note more than one classification per keyword is possible). Theclassification information provided by the user (20) is supplemented bya second classification that identifies the semantic map or mapsassociated with the keyword. The input from the user (20) is stored inthe keyword table (150) in the application database by enterprise beforeprocessing advances to a software block 257.

[0146] The software in block 257 checks the bot date table (149) anddeactivates any Internet text and linkage bots with creation datesbefore the current system date and retrieves information from the systemsettings table (140), the metadata mapping table (141) and the keywordtable (150). The software in block 257 then initializes Internet textand linkage bots for each field in the metadata mapping table (141) thatmapped to a keyword in accordance with the frequency specified by user(20) in the system settings table (140).

[0147] Bots are independent components of the application that havespecific tasks to perform. In the case of text and linkage bots, theirtasks are to locate, count, classify and extract keyword matches andlinkages from the Internet and then store their findings as itemvariables in a specified location. The classification includes both thefactor, element, enterprise or industry that the keyword is associatedwith and the context of the keyword mention. This dual classificationallows the system of the present invention to identify both the numberof times an enterprise element was mentioned and the context in whichthe enterprise element appeared. For example, the system might identifythe fact that an enterprise brand was mentioned 367 times in the mostrecent month and that 63% of the mentions were associated with afavorable semantic map. Each Internet text and linkage bot initializedby software block 257 will store the extracted data and the location,count and classification data it discovers in the classified text table(151) by enterprise. Multimedia data can be processed using these samebots if software to translate and parse the multimedia content isincluded in each bot. Every Internet text and linkage bot contains theinformation shown in Table 18. TABLE 18 1. Unique ID number (based ondate, hour, minute, second of creation) 2. Creation date (date, hour,minute, second) 3. Storage location 4. Mapping information 5. Home URL6. Organization 7. Enterprise 8. Keyword 9. Element, factor, enterpriseor industry 10. Semantic map

[0148] After being initialized, the text and linkage bots locate andclassify data from the Internet (40) in accordance with their programmedinstructions with the frequency specified by user (20) in the systemsettings table (140). As each text bot locates and classifies data fromthe Internet (40) processing advances to a software block 258 before thebot completes data storage. The software in block 258 checks to see ifall linkages keyword hits have been classified by element, factor orenterprise. If the software in block 258 does not find any unclassified“hits” or “links”, then the address, counts, dates and classified textare stored in the classified text table (151) by enterprise.Alternatively, if there are hits or links that haven't been classified,then processing advances to a block 259. The software in block 259prompts the user (20) via the identification and classification ruleswindow (703) to provide classification rules for each new hit or link.The information regarding the new classification rules is stored in thekeyword table (150) while the newly classified text and linkages arestored in the classified text table (151) by enterprise. It is worthnoting at this point that the activation and operation of bots where allfields map to the application database (50) continues. Only bots withunclassified fields will “wait” for user input before completing datastorage. The new classification rules will be used the next time botsare initialized in accordance with the frequency established by the user(20). In either event, system processing then passes on to a softwareblock 263.

[0149] The software in block 263 checks the bot date table (149) anddeactivates any text bots with creation dates before the current systemdate and retrieves information from the system settings table (140), themetadata mapping table (141) and the keyword table (150). The softwarein block 263 then initializes text bots for each field in the metadatamapping table (141) that mapped to a keyword in accordance with thefrequency specified by user (20) in the system settings table (140). Thetext bots use the same classification schema used for Internet text forclassifying text found in external and internal databases. Every botinitialized by software block 263 will store the extracted location,count, date and classification of data it discovers as item variables inthe classified text table (151) by enterprise. Every text bot containsthe information shown in Table 19. TABLE 19 1. Unique ID number (basedon date, hour, minute, second of creation) 2. Creation date (date, hour,minute, second) 3. Storage location 4. Mapping information 5.Organization 6. Enterprise 7. Data source 8. Keyword 9. Storage location10. Element, factor, enterprise or industry 11. Semantic map

[0150] After being initialized, the bots locate data from the externaldatabase (25) or the asset management system database (35) in accordancewith its programmed instructions with the frequency specified by user(20) in the system settings table (140). As each bot locates andextracts text data, processing advances to a software block 258 beforethe bot completes data storage. The software in block 258 checks to seeif all keyword hits are classified by element, factor, enterprise,industry and semantic map. If the software in block 258 does not findany unclassified “hits”, then the address, count and classified text arestored in the classified text table (151) by enterprise. Alternatively,if there are terms that have not been classified, then processingadvances to a block 259. The software in block 259 prompts the user (20)via the identification and classification rules window (703) to provideclassification rules for each new term. The information regarding thenew classification rules is stored in the keyword table (150) while thenewly classified text is stored in the classified text table (151) byenterprise. It is worth noting at this point that the activation andoperation of bots with classified data (50) continues. Only bots withunclassified fields “wait” for user input before completing datastorage. The new classification rules will be used the next time botsare initialized in accordance with the frequency established by the user(20). In either event, system processing then passes on to softwareblock 264.

[0151] The software in block 264 checks the system settings table (140)to see if there is geospatial data in the application database (50) andto determine which on-line geocoding service (Centrus™ from QM Soft orMapMarker™ from MapInfo) is being used. If geospatial data are not beingused, then processing advances to a block 269. Alternatively, if thesoftware in block 264 determines that geospatial data are being used,processing advances to a software block 265.

[0152] The software in block 265 prompts the user (20) via thegeospatial measure definitions window (710) to define the measures thatwill be used in evaluating the elements of value. After specifying themeasures, the user (20) is prompted to select geospatial loci for eachmeasure from the data already stored in the application database (50).The input from the user (20) is stored in the geospatial measures table(152) in the application database before processing advances to asoftware block 266.

[0153] The software in block 266 checks the bot date table (149) anddeactivates any geospatial bots with creation dates before the currentsystem date and retrieves information from the system settings table(140), the metadata mapping table (141) and the geospatial measurestable (152). The software in block 266 then initializes geospatial botsfor each field in the metadata mapping table (141) that mapped togeospatial data in the application database (50) in accordance with thefrequency specified by user (20) in the system settings table (140)before advancing processing to a software block 280.

[0154] Bots are independent components of the application that havespecific tasks to perform. In the case of geospatial bots, their tasksare to calculate item variables using a specified geocoding service,then store the measures in a specified location. Each geospatial botinitialized by software block 266 will store the item variable measuresit calculates in the application database table where the geospatialdata was found by enterprise. For example, calculated item variablesrelated to customer locations would be stored in the asset managementsystem table (148) for customer data. Tables that are likely to includegeospatial data include: the basic financial system table (143), theoperation system table (144), the human resource system table (145), theexternal database table (146), the advanced finance system table (147)and the asset system table (148). Every geospatial bot contains theinformation shown in Table 20. TABLE 20 1. Unique ID number (based ondate, hour, minute, second of creation) 2. Creation date (date, hour,minute, second) 3. Mapping information 4. Storage location 5.Organization 6. Enterprise 7. Geospatial locus 8. Geospatial measure 9.Geocoding service

[0155] After being activated, the geospatial bots locate data andcalculate measurements (which are descriptive item variables) inaccordance with their programmed instructions with the frequencyspecified by the user (20) in the system settings table (140). As eachgeospatial bot retrieves data and calculates the geospatial measuresthat have been specified, processing advances to a block 267 before thebot completes data storage. The software in block 267 checks to see ifall geospatial data located by the bot have been measured. If thesoftware in block 267 does not find any uncalculated measurement data,then the measurements are stored in the application database (50) byenterprise. Alternatively, if there are data elements where measureshave not been calculated, then processing advances to a block 268. Thesoftware in block 268 prompts the user (20) via the geospatial measuredefinition window (710) to provide measurement rules for each new term.The information regarding the new measurement rules is stored in thegeospatial measures table (152) while the newly calculated measurementsare stored in the appropriate table in the application database (50) byenterprise. It is worth noting at this point that the activation andoperation of bots that do not have unmeasured fields continues. Only thebots with uncalculated measurements “wait” for user input beforecompleting data storage. The new measurement rules will be used the nexttime bots are initialized in accordance with the frequency establishedby the user (20). In either event, system processing then passes on to asoftware block 269.

[0156] The software in block 269 checks the system settings table (140)to see if semantic mapping is being used. If semantic mapping is notbeing used, then processing advances to a block 281. Alternatively, ifthe software in block 269 determines that semantic mapping is beingused, processing advances to a software block 270.

[0157] The software in block 270 checks the bot date table (149) anddeactivates any inference bots with creation dates before the currentsystem date and retrieves information from the system settings table(140), the metadata mapping table (141), the keyword table (150) and theclassified text table (151). The software in block 270 then initializesinference bots for each keyword in the metadata mapping table (141) thatmapped to the classified text table (151) in the application database(50) in accordance with the frequency specified by user (20) in thesystem settings table (140).

[0158] Bots are independent components of the application that havespecific tasks to perform. In the case of inference bots, their task isto use Bayesian inference algorithms to determine the characteristicsthat give meaning to the text associated with keywords and classifiedtext previously stored in the application database (50). Every inferencebot contains the information shown in Table 21. TABLE 21 1. Unique IDnumber (based on date, hour, minute, second of creation) 2. Creationdate (date, hour, minute, second) 3. Mapping information 4. Storagelocation 5. Organization 6. Enterprise 7. Keyword 8. Classified textmapping information

[0159] After being activated, the inference bots determine thecharacteristics that give the text meaning in accordance with theirprogrammed instructions with the frequency specified by the user (20) inthe system settings table (140). The information defining thecharacteristics that give the text meaning is stored in the semantic maptable (180) in the application database (50) before processing advancesto block 272.

[0160] The software in block 272 checks the semantic map table (180) tosee if there are new semantic maps. If there are no new semantic maps,then processing advances to a block 281. Alternatively, if the softwarein block 272 determines that there are new semantic maps, thenprocessing returns to software block 255 and the processing describedpreviously for Internet, text and geospatial bots is repeated.

[0161] The software in block 281 checks: the basic financial systemtable (143), the operation system table (144), the human resource systemtable (145), the external database table (146), the advanced financesystem table (147), the asset system table (148), the classified texttable (151), the geospatial measures table (152), the supply chainsystem table (174) and the risk system table (176) to see if data aremissing from any of the periods required for system calculation. Thesoftware in block 202 previously calculated the range of required dates.If there are no data missing from any required period, then processingadvances to a software block 283. Alternatively, if there are missingdata for any field for any period, then processing advances to a block282.

[0162] The software in block 282, prompts the user (20) via the missingdata window (704) to specify the method to be used for filling theblanks for each item that is missing data. Options the user (20) canchoose from for filling the blanks include: the average value for theitem over the entire time period, the average value for the item over aspecified period, zero, the average of the preceding item and thefollowing item values and direct user input for each missing item. Ifthe user (20) does not provide input within a specified interval, thenthe default missing data procedure specified in the system settingstable (140) is used. When all the blanks have been filled and stored forall of the missing data, system processing advances to a block 283.

[0163] The software in block 283 calculates attributes by item for eachnumeric item variable in the basic financial system table (143), theoperation system table (144), the human resource system table (145), theexternal database table (146), the advanced finance system table (147),the asset system table (148), the supply chain system table (174) andthe risk system table (176). The attributes calculated in this stepinclude: summary data like cumulative total value; ratios like theperiod to period rate of change in value; trends like the rollingaverage value, comparisons to a baseline value like change from a prioryears level and time lagged values like the time lagged value of eachnumeric item variable. The software in block 283 calculates similarattributes for the text and geospatial item variables createdpreviously. The software in block 283 calculates attributes for eachdate item variable in the extracted text data and specified tables (143,144, 145, 146, 147, 148, 174 and 176) including summary data like timesince last occurrence and cumulative time since first occurrence; andtrends like average frequency of occurrence and the rolling averagefrequency of occurrence. The numbers derived from the item variables arecollectively referred to as “item performance indicators”. The softwarein block 283 also calculates pre-specified combinations of variablescalled composite variables for measuring the strength of the differentelements of value. The item performance indicators are stored in thetable where the item source data was obtained and the compositevariables are stored in the composite variables table (153) beforeprocessing advances to a block 284.

[0164] The software in block 284 uses attribute derivation algorithmssuch as the AQ program to create combinations of the variables that werenot pre-specified for combination. While the AQ program is used in thepreferred embodiment of the present invention, other attributederivation algorithms, such as the LINUS algorithms, may be used to thesame effect. The software creates these attributes using both itemvariables that were specified as “element” variables and item variablesthat were not. The resulting composite variables are stored in thecomposite variables table (153) before processing advances to a block285.

[0165] The software in block 285 derives external factor indicators foreach numeric data field defined in the external factor definition table(169). For example, external factors include: the ratio of enterpriseearnings to expected earnings, the number and amount of jury awards,commodity prices, the inflation rate, growth in g.d.p., enterpriseearnings volatility vs. industry average volatility, short and long terminterest rates, increases in interest rates, insider trading directionand levels, industry concentration, consumer confidence and theunemployment rate that have an impact on the market price of the equityfor an enterprise and/or an industry. The external factor indicatorsderived in this step include: summary data like cumulative totals,ratios like the period to period rate of change, trends like the rollingaverage value, comparisons to a baseline value like change from a prioryears price and time lagged data like time lagged earnings forecasts. Ina similar fashion the software in block 285 calculates external factorsfor each date field in the external factor definition table (169)including summary factors like time since last occurrence and cumulativetime since first occurrence; and trends like average frequency ofoccurrence and the rolling average frequency of occurrence. The numbersderived from numeric and date fields are collectively referred to as“factor performance indicators”. The software in block 285 alsocalculates pre-specified combinations of variables called compositefactors for measuring the strength of the different external factors.The external factors, factor performance indicators and the compositefactors are stored in the factor variables table (182) before processingadvances to a block 286.

[0166] The software in block 286 uses attribute derivation algorithms,such as the Linus algorithm, to create combinations of the factors thatwere not pre-specified for combination. While the Linus algorithm isused in the preferred embodiment of the present invention, otherattribute derivation algorithms, such as the AQ program, may be used tothe same effect. The software creates these attributes using bothexternal factors that were included in “composite factors” and externalfactors that were not. The resulting composite variables are stored inthe factor variables table (182) before processing advances to a block287.

[0167] The software in block 287 uses pattern-matching algorithms toassign pre-designated data fields for different elements of value topre-defined groups with numerical values. This type of analysis isuseful in classifying purchasing patterns and/or communications patternsas “heavy”, “light”, “moderate” or “sporadic”. This analysis is also beused to classify web site activity and advertising patterns in a similarfashion. The numeric values associated with the classifications are itemperformance indicators. They are stored in the application database (50)table where the item variables or factor variables they are derived fromare located before processing advances to a block 288.

[0168] The software in block 288 retrieves data from the metadatamapping table (141) and system settings table (140) as required tocreate and then stores detailed definitions for the segments of valueand the pre-defined components of value for the current operation in thesegment definition table (156) by enterprise. As discussed previously,there are up to five segments of value per enterprise—current operation,real options, derivatives, excess financial assets and market sentiment.The current operation is further subdivided into: a revenue component ofvalue that is not divided into sub-components, the expense value that isdivided into five sub-components: the cost of raw materials, the cost ofmanufacture or delivery of service, the cost of selling, the cost ofsupport and the cost of administration and the capital value that isdivided into six sub-components: cash, non-cash financial assets,production equipment, other assets, financial liabilities and equity inthe preferred embodiment. Different subdivisions of the components ofvalue can be used to the same effect. When data storage is complete,processing advances to a software block 291.

[0169] The software in block 291 checks the derivatives table (175) inthe application database (50) to see if there are historical values forall the derivatives stored in the table. Because SFAS 133 is still notfully implemented, some companies may not have data regarding the valueof their derivatives during a time period where data are required. Ifthere are values stored for all required time periods, then processingadvances to a software block 302 where the analysis of the extracteddata is started. Alternatively, if there are periods when the value ofone or more derivatives has not been stored, then processing advances toa software block 292. The software in block 292 retrieves the requireddata from the external database table (146), the external factors tableand the derivatives table (175) as required to value each derivativeusing a risk neutral valuation method for the time period or timeperiods that are missing values. The algorithms used for this analysiscan include Quasi Monte Carlo, equivalent Martingale or wavelets. Whenthe calculations are completed, the resulting values are stored in thederivatives table (175) by enterprise and processing advances to a block293.

[0170] The software in block 293 prompts the user (20) via the framedefinition window (705) to specify frames for analysis. Frames aresub-sets of each enterprise that can be analyzed at the value driverlevel separately. For example, the user (20) may wish to examine valueand risk by country, by division, by project, by action, by program orby manager. The software in block 293 saves the frame definitions theuser (20) specifies in the frame definition table (181) by enterprise inthe application database (50) before processing advances to a softwareblock 294.

[0171] The software in block 294 retrieves the segment, element andfactor variables from the: basic financial system (143), human resourcesystem table (145), external database table (146), advanced financesystem (147), asset system table (148), keyword table (150), classifiedtext table (151), geospatial measures table (152), composite variablestable (153), supply chain system table (174), derivatives table (175),risk system table (176), event risk table (178), financial forecaststable (179) and factor variables table (182) as required to assign framedesignations to every element and factor variable that was stored in theapplication database (50) in the prior processing steps in this stage(200) of processing. After storing the revised segment, element andfactor variables records in the same table they were retrieved from inthe application database (50), the software in the block retrieves thedefinitions from the element definition table (155), segment definitiontable (156) and external factor definition table (169), updates them toreflect the new frame definitions and saves them in the appropriratetable before processing advances to a software block 295.

[0172] The software in block 295 checks the: basic financial system(143), human resource system table (145), external database table (146),advanced finance system (147), asset system table (148), keyword table(150), classified text table (151), geospatial measures table (152),composite variables table (153), supply chain system table (174),derivatives table (175), risk system table (176), event risk table(178), financial forecasts table (179) and factor variables table (182)to see if there are frme assignments for all segment, element and factorvariables. If there are frame assignments for all variables, thenprocessing advances to a software block 302 where the analysis of theextracted data is started. Alternatively, if there are variables withoutframe assignments, then processing advances to a software block 296.

[0173] The software in block 296 retrieves variables from the basicfinancial system (143), human resource system table (145), externaldatabase table (146), advanced finance system (147), asset system table(148), keyword table (150), classified text table (151), geospatialmeasures table (152), composite variables table (153), supply chainsystem table (174), derivatives table (175), risk system table (176),event risk table (178), financial forecasts table (179) and factorvariables table (182) that don't have frame assignments and then promptsthe user (20) via the frame assignment window (705) to specify frameassignments for these variables. The software in block 296 saves theframe assignments the user (20) specifies as part of the data record forthe variable in the table where the variable was retrieved from byenterprise in the application database (50) before processing advancesto software block 302 to begin the value analysis of the extracted data.

[0174] Value Analysis

[0175] The flow diagrams in FIG. 6A, FIG. 6B and FIG. 6C detail theprocessing that is completed by the portion of the application software(300) that continually values the segments of value by enterprise. Thisportion of the application software (300) also generates a matrixquantifying the impact of elements of value and external factors on thesegments of value for each enterprise within the organization (see FIG.9) by creating and activating analysis bots that:

[0176] 1) Identify the factor variables, factor performance indicatorsand composite variables for each external factor that drive: three ofthe segments of value—current operation, derivatives and excessfinancial assets—as well as the components of current operation value(revenue, expense and changes in capital);

[0177] 2) Identify the item variables, item performance indicators andcomposite variables for each element and sub-element of value thatdrive: three segments of value—current operation, derivatives andfinancial assets—as well as the components of current operation value(revenue, expense and changes in capital);

[0178] 3) Create vectors that summarize the impact of the factorvariables, factor performance indicators and composite variables foreach external factor;

[0179] 4) Create vectors that summarize the performance of the itemvariables, item performance indicators and composite variables for eachelement of value and sub-element of value in driving segment value;

[0180] 5) Determine the expected life of each element of value andsub-element of value;

[0181] 6) Determine the current operation value, excess financial assetvalue and derivative value, revenue component value, expense componentvalue and capital component value of said current operations using theinformation prepared in the previous stages of processing;

[0182] 7) Specify and optimize causal predictive models to determine therelationship between the vectors generated in steps 3 and 4 and thethree segments of value, current operation, derivatives and financialassets, as well as the components of current operation value (revenue,expense and changes in capital);

[0183] 8) Determine the appropriate discount rate on the basis ofrelative causal element strength, value the enterprise real options andcontingent liabilities and determine the contribution of each element toreal option valuation;

[0184] 9) Determine the best causal indicator for enterprise stock pricemovement, calculate market sentiment and analyze the causes of marketsentiment; and

[0185] 10) Combine the results of all prior stages of processing todetermine the value of each element, sub-element and factor for eachenterprise and the organization.

[0186] Each analysis bot generally normalizes the data being analyzedbefore processing begins. While the processing in the preferredembodiment includes an analysis of all five segments of value for theorganization, it is to be understood that the system of the presentinvention can complete calculations for any combination of the fivesegments. For example, when a company is privately held it does not havea market price and as a result the market sentiment segment of value isnot analyzed.

[0187] Processing in this portion of the application begins in softwareblock 302. The software in block 302 checks the system settings table(140) in the application database (50) to determine if the currentcalculation is a new calculation or a structure change. If thecalculation is not a new calculation or a structure change, thenprocessing advances to a software block 315. Alternatively, if thecalculation is new or a structure change, then processing advances to asoftware block 303.

[0188] The software in block 303 retrieves data from the system settingstable (140), the meta data mapping table (141), the asset system table(148), the element definition table (155) and the frame definition table(181) and then assigns item variables, item performance indicators andcomposite variables to each element of value identified in the systemsettings table (140) using a three-step process. First, item variables,item performance indicators and composite variables are assigned toelements of value based on the asset management system they correspondto (for example, all item variables from a brand management system andall item performance indicators and composite variables derived frombrand management system item variables are assigned to the brand elementof value). Second, pre-defined composite variables are assigned to theelement of value they were assigned to measure in the metadata mappingtable (141). Finally, item variables, item performance indicators andcomposite variables identified by the text and geospatial bots areassigned to elements on the basis of their element classifications. Ifany item variables, item performance indicators or composite variablesare unassigned at this point they are assigned to a going concernelement of value. After the assignment of variables and indicators toelements is complete, the resulting assignments are saved to the elementdefinition table (155) by enterprise and processing advances to a block304.

[0189] The software in block 304 retrieves data from the meta datamapping table (141), the external factor definition table (169) and theframe definition table (181) and then assigns factor variables, factorperformance indicators and composite factors to each external factor.Factor variables, factor performance indicators and composite factorsidentified by the text and geospatial bots are then assigned to factorson the basis of their factor classifications. The resulting assignmentsare saved to external factor definition table (169) by enterprise andprocessing advances to a block 305.

[0190] The software in block 305 checks the system settings table (140)in the application database (50) to determine if any of the enterprisesin the organization being analyzed have market sentiment segments. Ifthere are market sentiment segments for any enterprise, then processingadvances to a block 306. Alternatively, if there are no market pricesfor equity for any enterprise, then processing advances to a softwareblock 308.

[0191] The software in block 306 checks the bot date table (149) anddeactivates any market value indicator bots with creation dates beforethe current system date. The software in block 306 then initializesmarket value indicator bots in accordance with the frequency specifiedby the user (20) in the system settings table (140). The bot retrievesthe information from the system settings table (140), the metadatamapping table (141) and the external factor definition table (169)before saving the resulting information in the application database(50).

[0192] Bots are independent components of the application that havespecific tasks to perform. In the case of market value indicator botstheir primary task is to identify the best market value indicator(price, relative price, yield, first derivative of price change orsecond derivative of price change) for the time period being examined.The market value indicator bots select the best value indicator bygrouping the S&P 500 using each of the five value indicators with aKohonen neural network. The resulting clusters are then compared to theknown groupings of the S&P 500. The market value indicator that producedthe clusters that most closely match the know S&P 500 is selected as themarket value indicator. Every market value indicator bot contains theinformation shown in Table 22. TABLE 22 1. Unique ID number (based ondate, hour, minute, second of creation) 2. Creation date (date, hour,minute, second) 3. Mapping information 4. Storage location 5.Organization 6. Enterprise

[0193] When bot in block 306 have identified and stored the best marketvalue indicator in the external factor definition table (169),processing advances to a block 307.

[0194] The software in block 307 checks the bot date table (149) anddeactivates any temporal clustering bots with creation dates before thecurrent system date. The software in block 307 then initializes a bot inaccordance with the frequency specified by the user (20) in the systemsettings table (140). The bot retrieves information from the systemsettings table (140), the metadata mapping table (141) and the externaldatabase table (146) as required and define regimes for the enterprisemarket value before saving the resulting cluster information in theapplication database (50).

[0195] Bots are independent components of the application that havespecific tasks to perform. In the case of temporal clustering bots,their primary task is to segment the market price data by enterpriseusing the market value indicator selected by the bot in block 306 intodistinct time regimes that share similar characteristics. The temporalclustering bot assigns a unique identification (id) number to each“regime” it identifies and stores the unique id numbers in the clusterid table (157). Every time period with data are assigned to one of theregimes. The cluster id for each regime is saved in the data record foreach element variable and factor variable in the table where it residesby enterprise. If there are enterprises in the organization that don'thave market sentiment calculations, then the time regimes from theprimary enterprise specified by the user in the system settings table(140) are used in labeling the data for the other enterprises. After theregimes are identified, the element and factor variables for eachenterprise are segmented into a number of regimes less than or equal tothe maximum specified by the user (20) in the system settings table(140). The time periods are segmented for each enterprise with a marketvalue using a competitive regression algorithm that identifies anoverall, global model before splitting the data and creating new modelsfor the data in each partition. If the error from the two models isgreater than the error from the global model, then there is only oneregime in the data. Alternatively, if the two models produce lower errorthan the global model, then a third model is created. If the error fromthree models is lower than from two models then a fourth model is added.The process continues until adding a new model does not improveaccuracy. Other temporal clustering algorithms may be used to the sameeffect. Every temporal clustering bot contains the information shown inTable 23. TABLE 23 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (date, hour, minute, second) 3.Mapping information 4. Storage location 5. Maximum number of clusters 6.Organization 7. Enterprise

[0196] When bots in block 307 have identified and stored regimeassignments for all time periods with data by enterprise, processingadvances to a software block 308.

[0197] The software in block 308 checks the bot date table (149) anddeactivates any variable clustering bots with creation dates before thecurrent system date. The software in block 308 then initializes bots asrequired for each element of value and external factor by enterprise.The bots: activate in accordance with the frequency specified by theuser (20) in the system settings table (140), retrieve the informationfrom the system settings table (140), the metadata mapping table (141),the element definition table (155) and external factor definition table(169) as required and define segments for the element variables andfactor variables before saving the resulting cluster information in theapplication database (50).

[0198] Bots are independent components of the application that havespecific tasks to perform. In the case of variable clustering bots,their primary task is to segment the element variables and factorvariables into distinct clusters that share similar characteristics. Theclustering bot assigns a unique id number to each “cluster” itidentifies and stores the unique id numbers in the cluster id table(157). Every item variable for every element of value is assigned to oneof the unique clusters. The cluster id for each variable is saved in thedata record for each variable in the table where it resides. In asimilar fashion, every factor variable for every external factor isassigned to a unique cluster. The cluster id for each variable is savedin the data record for the factor variable. The item variables andfactor variables are segmented into a number of clusters less than orequal to the maximum specified by the user (20) in the system settingstable (140). The data are segmented using the “default” clusteringalgorithm the user (20) specified in the system settings table (140).The system of the present invention provides the user (20) with thechoice of several clustering algorithms including: an unsupervised“Kohonen” neural network, neural network, decision tree, support vectormethod, K-nearest neighbor, expectation maximization (EM) and thesegmental K-means algorithm. For algorithms that normally require thenumber of clusters to be specified, the bot will iterate the number ofclusters until it finds the cleanest segmentation for the data. Everyvariable clustering bot contains the information shown in Table 24.TABLE 24 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Element of value, sub element ofvalue or external factor 6. Clustering algorithm type 7. Organization 8.Enterprise 9. Maximum number of clusters 10. Variable 1 . . . to 10 + n.Variable n

[0199] When bots in block 308 have identified and stored clusterassignments for the variables associated with each element of value,sub-element of value or external factor, processing advances to asoftware block 309.

[0200] The software in block 309 checks the bot date table (149) anddeactivates any predictive model bots with creation dates before thecurrent system date. The software in block 309 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), the element definition table (155), the segment definitiontable (156) and the external factor definition table (169) as requiredto initialize predictive model bots for each component of value.

[0201] Bots are independent components of the application that havespecific tasks to perform. In the case of predictive model bots, theirprimary task is to determine the relationship between the element andfactor variables and the derivative segment of value, the excessfinancial asset segment of value and the current operation segment ofvalue by enterprise. The predictive model bots also determine therelationship between the element variables and factor variablescomponents of current operation value and sub-components of currentoperation value by enterprise. Predictive model bots are initialized foreach component of value, sub-component of value, derivative segment andexcess financial asset segment by enterprise. They are also initializedfor each cluster and regime of data in accordance with the cluster andregime assignments specified by the bots in blocks 307 and 308 byenterprise. A series of predictive model bots is initialized at thisstage because it is impossible to know in advance which predictive modeltype will produce the “best” predictive model for the data from eachcommercial enterprise. The series for each model includes 12 predictivemodel bot types: neural network; CART; GARCH, projection pursuitregression; generalized additive model (GAM), redundant regressionnetwork; rough-set analysis, boosted Naive Bayes Regression; MARS;linear regression; support vector method and stepwise regression.Additional predictive model types can be used to the same effect. Thesoftware in block 309 generates this series of predictive model bots forthe enterprise as shown in Table 25. TABLE 25 Predictive models byenterprise level Enterprise: Variables* relationship to enterprise cashflow (revenue − expense + capital change) Variables* relationship toenterprise revenue component of value Variables* relationship toenterprise expense subcomponents of value Variables* relationship toenterprise capital change subcomponents of value Variables* relationshipto derivative segment of value Variables* relationship to excessfinancial asset segment of value Element of Value: Sub-element of valuevariables relationship to element of value

[0202] Every predictive model bot contains the information shown inTable 26. TABLE 26 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (date, hour, minute, second) 3.Mapping information 4. Storage location 5. Organization 6. Enterprise 7.Global or Cluster (ID) and/or Regime (ID) 8. Segment (Derivative, ExcessFinancial Asset or Current Operation) 9. Element, sub-element orexternal factor 10. Predictive Model Type

[0203] After predictive model bots are initialized, the bots activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). Once activated, the bots retrieve the requireddata from the appropriate table in the application database (50) andrandomly partition the element or factor variables into a training setand a test set. The software in block 309 uses “bootstrapping” where thedifferent training data sets are created by re-sampling with replacementfrom the original training set so data records may occur more than once.After the predictive model bots complete their training and testing,processing advances to a block 310.

[0204] The software in block 310 determines if clustering improved theaccuracy of the predictive models generated by the bots in softwareblock 309 by enterprise. The software in block 310 uses a variableselection algorithm such as stepwise regression (other types of variableselection algorithms can be used) to combine the results from thepredictive model bot analyses for each type of analysis—with and withoutclustering—to determine the best set of variables for each type ofanalysis. The type of analysis having the smallest amount of error asmeasured by applying the mean squared error algorithm to the test datais given preference in determining the best set of variables for use inlater analysis. There are four possible outcomes from this analysis asshown in Table 27. TABLE 27 1. Best model has no clustering 2. Bestmodel has temporal clustering, no variable clustering 3. Best model hasvariable clustering, no temporal clustering 4. Best model has temporalclustering and variable clustering

[0205] If the software in block 310 determines that clustering improvesthe accuracy of the predictive models for an enterprise, then processingadvances to a software block 313. Alternatively, if clustering does notimprove the overall accuracy of the predictive models for an enterprise,then processing advances to a software block 311.

[0206] The software in block 311 uses a variable selection algorithmsuch as stepwise regression (other types of variable selectionalgorithms can be used) to combine the results from the predictive modelbot analyses for each model to determine the best set of variables foreach model. The models having the smallest amount of error as measuredby applying the mean squared error algorithm to the test data is givenpreference in determining the best set of variables. As a result of thisprocessing, the best set of variables contain: the item variables,factor variables, item performance indicators, factor performanceindications, composite variables and compoiste factors that correlatemost strongly with changes in the three segments being analyzed and thethree components of value. The best set of variables will hereinafter bereferred to as the “value drivers”. Eliminating low correlation factorsfrom the initial configuration of the vector creation algorithmsincreases the efficiency of the next stage of system processing. Othererror algorithms alone or in combination may be substituted for the meansquared error algorithm. After the best set of variables have beenselected and stored in the element variables table (158) or factorvariables table (182) for all models at all levels for each enterprisein the organization, the software in block 311 tests the independence ofthe value drivers at the enterprise, external factor, element andsub-element level before processing advances to a block 312.

[0207] The software in block 312 checks the bot date table (149) anddeactivates any causal predictive model bots with creation dates beforethe current system date. The software in block 312 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), the segment definition table (156), the element variablestable (158) and the factor variables table (182) as required toinitialize causal predictive model bots for each element of value,sub-element of value and external factor in accordance with thefrequency specified by the user (20) in the system settings table (140).

[0208] Bots are independent components of the application that havespecific tasks to perform. In the case of causal predictive model bots,their primary task is to refine the element and factor variableselection to reflect only causal variables. (Note: these variables aresummed together to value an element when they are interdependent). Aseries of causal predictive model bots are initialized at this stagebecause it is impossible to know in advance which causal predictivemodel will produce the “best” vector for the best fit variables fromeach model. The series for each model includes five causal predictivemodel bot types: Tetrad, MML, LaGrange, Bayesian and path analysis. Thesoftware in block 312 generates this series of causal predictive modelbots for each set of variables stored in the element variables table(158) and factor variables table (182) in the previous stage inprocessing. Every causal predictive model bot activated in this blockcontains the information shown in Table 28. TABLE 28 1. Unique ID number(based on date, hour, minute, second of creation) 2. Creation date(date, hour, minute, second) 3. Mapping information 4. Storage location5. Component or subcomponent of value 6. Element, sub-element orexternal factor 7. Variable set 8. Causal predictive model type 9.Organization 10. Enterprise

[0209] After the causal predictive model bots are initialized by thesoftware in block 312, the bots activate in accordance with thefrequency specified by the user (20) in the system settings table (140).Once activated, they retrieve the required information for each modeland sub-divide the variables into two sets, one for training and one fortesting. After the causal predictive model bots complete theirprocessing for each model, the software in block 312 uses a modelselection algorithm to identify the model that best fits the data foreach element of value, sub-element of value and external factor beinganalyzed. For the system of the present invention, a cross validationalgorithm is used for model selection. The software in block 312 savesthe best fit causal factors in the vector table (159) by enterprise inthe application database (50) and processing advances to a block 318.

[0210] The software in block 318 tests the value drivers to see if thereis interaction between elements, between elements and external factorsor between external factors by enterprisse. The software in this blockidentifies interaction by evaluating a chosen model based onstochastic-driven pairs of value-driver subsets. If the accuracy of sucha model is higher that the accuracy of statistically combined modelstrained on attribute subsets, then the attributes from subsets areconsidered to be interacting and then they form an interacting set. Ifthe software in block 318 does not detect any value driver interactionor missing variables for each enterprise, then system processingadvances to a block 323. Alternatively, if missing data or value driverinteractions across elements are detected by the software in block 318for one or more enterprise, then processing advances to a software block321.

[0211] If software in block 310 determines that clustering improvespredictive model accuracy, then processing advances to block 313 asdescribed previously. The software in block 313 uses a variableselection algorithm such as stepwise regression (other types of variableselection algorithms can be used) to combine the results from thepredictive model bot analyses for each model, cluster and/or regime todetermine the best set of variables for each model. The models havingthe smallest amount of error as measured by applying the mean squarederror algorithm to the test data is given preference in determining thebest set of variables. As a result of this processing, the best set ofvariables contains: the element variables and factor variables thatcorrelate most strongly with changes in the components of value. Thebest set of variables will hereinafter be referred to as the “valuedrivers”. Eliminating low correlation factors from the initialconfiguration of the vector creation algorithms increases the efficiencyof the next stage of system processing. Other error algorithms alone orin combination may be substituted for the mean squared error algorithm.After the best set of variables have been selected and stored in theelement variables table (158) or the factor variables table (182) forall models at all levels by enterprise, the software in block 313 teststhe independence of the value drivers at the enterprise, element,sub-element and external factor level before processing advances to ablock 314.

[0212] The software in block 314 checks the bot date table (149) anddeactivates any causal predictive model bots with creation dates beforethe current system date. The software in block 314 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), the segment definition table (156), the element variablestable (158) and the factor variables table (182) as required toinitialize causal predictive model bots for each element of value,sub-element of value and external factor at every level in accordancewith the frequency specified by the user (20) in the system settingstable (140).

[0213] Bots are independent components of the application that havespecific tasks to perform. In the case of causal predictive model bots,their primary task is to refine the element and factor variableselection to reflect only causal variables. (Note: these variables aregrouped together to represent a single element vector when they aredependent). In some cases it may be possible to skip the correlationstep before selecting causal the item variables, factor variables, itemperformance indicators, factor performance indicators, compositevariables and composite factors. A series of causal predictive modelbots are initialized at this stage because it is impossible to know inadvance which causal predictive model will produce the “best” vector forthe best fit variables from each model. The series for each modelincludes four causal predictive model bot types: Tetrad, LaGrange,Bayesian and path analysis. The software in block 314 generates thisseries of causal predictive model bots for each set of variables storedin the element variables table (158) in the previous stage inprocessing. Every causal predictive model bot activated in this blockcontains the information shown in Table 29. TABLE 29 1. Unique ID number(based on date, hour, minute, second of creation) 2. Creation date(date, hour, minute, second) 3. Mapping information 4. Storage location5. Component or subcomponent of value 6. Cluster (ID) and/or Regime (ID)7. Element, sub-element or external factor 8. Variable set 9.Organization 10. Enterprise 11. Causal predictive model type

[0214] After the causal predictive model bots are initialized by thesoftware in block 314, the bots activate in accordance with thefrequency specified by the user (20) in the system settings table (140).Once activated, they retrieve the required information for each modeland sub-divide the variables into two sets, one for training and one fortesting. The same set of training data is used by each of the differenttypes of bots for each model. After the causal predictive model botscomplete their processing for each model, the software in block 314 usesa model selection algorithm to identify the model that best fits thedata for each element, sub-element or external factor being analyzed bymodel and/or regime by enterprise. For the system of the presentinvention, a cross validation algorithm is used for model selection. Thesoftware in block 314 saves the best fit causal factors in the vectortable (159) by enterprise in the application database (50) andprocessing advances to block 318. The software in block 318 tests thevalue drivers to see if there are “missing” value drivers that areinfluencing the results as well as testing to see if there areinteractions (dependencies) across elements. If the software in block318 does not detect any missing data or value driver interactions acrosselements, then system processing advances to a block 323. Alternatively,if missing data or value driver interactions across elements aredetected by the software in block 318, then processing advances to asoftware block 321.

[0215] The software in block 321 prompts the user (20) via the structurerevision window (710) to adjust the specification(s) for the affectedelements of value, sub-elements of value or external factors as requiredto minimize or eliminate the interaction. At this point the user (20)has the option of specifying that one or more elements of value, subelements of value and/or external factors be combined for analysispurposes (element combinations and/or factor combinations) for eachenterprise where there is interaction between elements and/or factors.The user (20) also has the option of specifying that the elements orexternal factors that are interacting will be valued by summing theimpact of their value drivers. Finally, the user (20) can chose tore-assign a value driver to a new element of value to eliminate theinter-dependency. This is the preferred solution when theinter-dependent value driver is included in the going concern element ofvalue. Elements and external factors that will be valued by summingtheir value drivers will not have vectors generated. After the inputfrom the user (20) is saved in the system settings table (140), theelement definition table (155) and the external factor definition table(169) system processing advances to a software block 323. The softwarein block 323 checks the system settings table (140), the elementdefinition table (155) and/or the external factor definition table (169)to see if there any changes in structure. If there have been changes inthe structure, then processing advances to a block 205 and the systemprocessing described previously is repeated. Alternatively, if there areno changes in structure, then processing advances to a block 325.

[0216] The software in block 325 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new one. If the calculation is new, then processing advances to asoftware block 326. Alternatively, if the calculation is not a newcalculation, then processing advances to a software block 333.

[0217] The software in block 326 checks the bot date table (149) anddeactivates any industry rank bots with creation dates before thecurrent system date. The software in block 326 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), and the vector table (159) as required to initializeindustry rank bots for the enterprise and for the industry in accordancewith the frequency specified by the user (20) in the system settingstable (140).

[0218] Bots are independent components of the application that havespecific tasks to perform. In the case of industry rank bots, theirprimary task is to determine the relative position of each enterprisebeing evaluated on element variables identified in the previousprocessing step. (Note: these variables are grouped together when theyare interdependent). The industry rank bots use ranking algorithms suchas Data Envelopment Analysis (hereinafter, DEA) to determine therelative industry ranking of the enterprise being examined. The softwarein block 326 generates industry rank bots for each enterprise beingevaluated. Every industry rank bot activated in this block contains theinformation shown in Table 30. TABLE 30 1. Unique ID number (based ondate, hour, minute, second of creation) 2. Creation date (date, hour,minute, second) 3. Mapping information 4. Storage location 5. Rankingalgorithm 6. Organization 7. Enterprise

[0219] After the industry rank bots are initialized by the software inblock 326, the bots activate in accordance with the frequency specifiedby the user (20) in the system settings table (140). Once activated,they retrieve the item variables, item performance indicators, andcomposite variables from the application database (50) and sub-dividesthem into two sets, one for training and one for testing. After theindustry rank bots develop and test their rankings, the software inblock 326 saves the industry rankings in the vector table (159) byenterprise in the application database (50) and processing advances to ablock 327. The industry rankings are item variables.

[0220] The software in block 327 checks the bot date table (149) anddeactivates any vector generation bots with creation dates before thecurrent system date. The software in block 327 then initializes bots foreach element of value, sub-element of value and external factor for eachenterprise in the organization. The bots activate in accordance with thefrequency specified by the user (20) in the system settings table (140),retrieve the information from the system settings table (140), themetadata mapping table (141), the segment definition table (156) and theelement variables table (158) as required to initialize vectorgeneration bots for each element of value and sub-element of value inaccordance with the frequency specified by the user (20) in the systemsettings table (140).

[0221] Bots are independent components of the application that havespecific tasks to perform. In the case of vector generation bots, theirprimary task is to produce formulas, (hereinafter, vectors) thatsummarize the relationship between the causal element variables orcausal factor variables and changes in the component or sub-component ofvalue being examined for each enterprise. The causal element variablesmay be grouped by element of value, sub-element of value, externalfactor, factor combination or element combination. As discussedpreviously, the vector generation step is skipped for elements andfactors where the user has specified that value driver impacts will bemathematically summed to determine the value of the element or factor.The vector generation bots use induction algorithms to generate thevectors. Other vector generation algorithms can be used to the sameeffect. The software in block 327 generates a vector generation bot foreach set of variables stored in the element variables table (158) andfactor variables table (182). Every vector generation bot contains theinformation shown in Table 31. TABLE 31 1. Unique ID number (based ondate, hour, minute, second of creation) 2. Creation date (date, hour,minute, second) 3. Mapping information 4. Storage location 5.Organization 6. Enterprise 7. Element, sub-element, element combination,factor or factor combination 8. Component or sub-component of value 9.Factor 1 . . . to 9 + n. Factor n

[0222] When bots in block 327 have identified and stored vectors for alltime periods with data for all the elements, sub-elements, elementcombination, factor combination or external factor where vectors arebeing calculated in the vector table (163) by enterprise, processingadvances to a software block 329.

[0223] The software in block 329 checks the bot date table (149) anddeactivates any financial factor bots with creation dates before thecurrent system date. The software in block 329 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), the element definition table (155), the element variablestable (158), the external factor definition table (169), the derivativestable (175), the financial forecasts table (179) and the factorvariables table (182) as required to initialize causal external factorbots for the enterprise and the relevant industry in accordance with thefrequency specified by the user (20) in the system settings table (140).

[0224] Bots are independent components of the application that havespecific tasks to perform. In the case of financial factor bots, theirprimary task is to identify elements of value, value drivers andexternal factors that are causal factors for changes in the value of:derivatives, financial assets, enterprise equity and industry equity.The causal factors for enterprise equity and industry equity are thosethat drive changes in the value indicator identified by the valueindicator bots. A series of financial factor bots are initialized atthis stage because it is impossible to know in advance which causalfactors will produce the “best” model for every derivative, financialasset, enterprise or industry. The series for each model includes fivecausal predictive model bot types: Tetrad, LaGrange, MML, Bayesian andpath analysis. Other causal predictive models can be used to the sameeffect. The software in block 329 generates this series of causalpredictive model bots for each set of variables stored in the elementvariables table (158) and factor variables table (182) in the previousstage in processing by enterprise. Every financial factor bot activatedin this block contains the information shown in Table 32 TABLE 32 1.Unique ID number (based on date, hour, minute, second of creation) 2.Creation date (date, hour, minute, second) 3. Mapping information 4.Storage location 5. Element, value driver or external factor 6.Organization 7. Enterprise 8. Type: derivatives, financial assets,enterprise equity or industry equity 9. Value indicator (price, relativeprice, first derivative, etc.) for enterprise and industry only 10.Causal predictive model type

[0225] After the software in block 329 initializes the financial factorbots, the bots activate in accordance with the frequency specified bythe user (20) in the system settings table (140). Once activated, theyretrieve the required information and sub-divide the data into two sets,one for training and one for testing. The same set of training data isused by each of the different types of bots for each model. After thefinancial factor bots complete their processing for each segment ofvalue, enterprise and industry, the software in block 329 uses a modelselection algorithm to identify the model that best fits the data foreach. For the system of the present invention, a cross validationalgorithm is used for model selection. The software in block 329 savesthe best fit causal factors in the factor variables table (182) byenterprise and the best fit causal elements and value drivers in theelement variables table (158) by enteprise and processing advances to ablock 330. The software in block 330 tests to see if there are “missing”causal factors, elements or value drivers that are influencing theresults by enterprise. If the software in block 330 does not detect anymissing factors, elements or value drivers, then system processingadvances to a block 331. Alternatively, if missing factors, elements orvalue drivers are detected by the software in block 330, then processingreturns to software block 321 and the processing described in thepreceding section is repeated.

[0226] The software in block 331 checks the bot date table (149) anddeactivates any option bots with creation dates before the currentsystem date. The software in block 331 then retrieves the informationfrom the system settings table (140), the metadata mapping table (141),the basic financial system database (143), the external database table(146), the advanced finance system table (147) and the vector table(163) as required to initialize option bots for the enterprise.

[0227] Bots are independent components of the application that havespecific tasks to perform. In the case of option bots, their primarytasks are to calculate the discount rate to be used for valuing the realoptions and contingent liabilities and to value the real options andcontingent liabilities for the enterprise. If the user (20) has chosento include industry options, then option bots will be initialized forindustry options as well. The discount rate for enterprise real optionsis calculated by adding risk factors for each causal element to a basediscount rate. A two step process determines the risk factor for eachcausal element. The first step in the process divides the maximum realoption discount rate (specified by the user in system settings) by thenumber of causal elements. The second step in the process determines ifthe enterprise is highly rated on the causal elements using rankingalgorithms like DEA and determines an appropriate risk factor. If theenterprise is highly ranked on the soft asset, then the discount rate isincreased by a relatively small amount for that causal element.Alternatively, if the enterprise has a low ranking on a causal element,then the discount rate is increased by a relatively large amount forthat causal element as shown below in Table 33. For options that arejoint options enabled by the two or more enterprises within theorganization, the same general procedure will be used, however, therelative strength of the different enterprises may be substituted forrelative causal element strength in determining the appropriate discountrate. TABLE 33 Maximum discount rate = 50%, Causal elements = 5 Maximumrisk factor/soft asset = 50%/5 = 10% Industry Rank on Soft Asset % ofMaximum 1  0% 2 25% 3 50% 4 75% 5 or higher 100%  Causal element:Relative Rank Risk Factor Brand 1  0% Channel 3  5% ManufacturingProcess 4 7.5%  Strategic Alliances 5 10% Vendors 2 2.5%  Subtotal 25%Base Rate 12% Discount Rate 37%

[0228] The discount rate for industry options is calculated using atraditional total cost of capital approach that includes the cost ofrisk capital in a manner that is well known. After the appropriatediscount rates are determined, the value of each real option andcontingent liability is calculated using the specified algorithms in amanner that is well known. The real option can be valued using a numberof algorithms including Black Scholes, binomial, neural network ordynamic programming algorithms. The industry option bots use theindustry rankings from prior processing block to determine an allocationpercentage for industry options. The more dominant the enterprise, asindicated by the industry rank for the element indicators, the greaterthe allocation of industry real options. Every option bot contains theinformation shown in Table 34. TABLE 34 1. Unique ID number (based ondate, hour, minute, second of creation) 2. Creation date (date, hour,minute, second) 3. Mapping information 4. Storage location 5.Organization 6. Industry or Enterprise 7. Real option type (Industry orEnterprise) 8. Real option algorithm (Black Scholes, Binomial,Quadranomial, Dynamic Program, etc.)

[0229] After the option bots are initialized, they activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). After being activated, the bots retrieveinformation as required to complete the option valuations. When they areused, industry option bots go on to allocate a percentage of thecalculated value of industry options to the enterprise on the basis ofcausal element strength. After the value of the real option, contingentliability or allocated industry option is calculated the resultingvalues are then saved in the real option value table (162) in theapplication database (50) by enterprise before processing advances to ablock 332.

[0230] The software in block 332 checks the bot date table (149) anddeactivates any cash flow bots with creation dates before the currentsystem date. The software in the block then retrieves the informationfrom the system settings table (140), the metadata mapping table (141),the advanced finance system table (147) and the segment definition table(156) as required to initialize cash flow bots for each enterprise inaccordance with the frequency specified by the user (20) in the systemsettings table (140).

[0231] Bots are independent components of the application that havespecific tasks to perform. In the case of cash flow bots, their primarytasks are to calculate the cash flow for each enterprise for every timeperiod where data are available and to forecast a steady state cash flowfor each enterprise in the organization. Cash flow is calculated usingthe forecast revenue, expense, capital change and depreciation dataretrieved from the advanced finance system table (147) with a well-knownformula where cash flow equals period revenue minus period expense plusthe period change in capital plus non-cash depreciation/amortization forthe period. The steady state cash flow for each enterprise is calculatedfor the enterprise using forecasting methods identical to thosedisclosed previously in U.S. Pat. No. 5,615,109 to forecast revenue,expenses, capital changes and depreciation separately before calculatingthe cash flow. Every cash flow bot contains the information shown inTable 35. TABLE 35 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (date, hour, minute, second) 3.Mapping information 4. Storage location 5. Organization 6. Enterprise

[0232] After the cash flow bots are initialized, the bots activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). After being activated the bots, retrieve theforecast data for each enterprise from the advanced finance system table(147) and then calculate a steady state cash flow forecast byenterprise. The resulting values by period for each enterprise are thenstored in the cash flow table (161) in the application database (50)before processing advances to a block 333.

[0233] The software in block 333 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change, then processing advances to asoftware block 341. Alternatively, if the calculation Is new or astructure change, then processing advances to a software block 343.

[0234] The software in block 341 uses the cash flow by period data fromthe cash flow table (161) and the calculated requirement for workingcapital to calculate the value of excess financial assets for every timeperiod by enterprise and stores the results of the calculation in thefinancial forecasts table (179) in the application database beforeprocessing advances to a block 342.

[0235] The software in block 342 checks the bot date table (149) anddeactivates any financial value bots with creation dates before thecurrent system date. The software in block 342 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), the element definition table (155), the element variablestable (158), the external factor definition table (169), the derivativestable (175) the financial forecasts table (179) and the factor variablestable (182) as required to initialize financial value bots for thederivatives and excess financial assets in accordance with the frequencyspecified by the user (20) in the system settings table (140).

[0236] Bots are independent components of the application that havespecific tasks to perform. In the case of financial value bots, theirprimary task is to determine the relative contribution of element dataand factor data identified in previous stages of processing on the valueof derivatives and excess financial assets by enterprise. The system ofthe present invention uses 12 different types of predictive models todetermine relative contribution: neural network; CART; projectionpursuit regression; generalized additive model (GAM); GARCH; MMDR;redundant regression network; boosted Nafve Bayes Regression; thesupport vector method; MARS; linear regression; and stepwise regression.The model having the smallest amount of error as measured by applyingthe mean squared error algorithm to the test data is the best fit model.The “relative contribution algorithm” used for completing the analysisvaries with the model that was selected as the “best-fit” as describedpreviously. Every financial value bot activated in this block containsthe information shown in Table 36. TABLE 36 1. Unique ID number (basedon date, hour, minute, second of creation) 2. Creation date (date, hour,minute, second) 3. Mapping information 4. Storage location 5.Organization 6. Enterprise 7. Derivative or Excess Financial Asset 8.Element Data or Factor Data 9. Predictive model type

[0237] After the software in block 342 initializes the financial valuebots, the bots activate in accordance with the frequency specified bythe user (20) in the system settings table (140). Once activated, theyretrieve the required information and sub-divide the data into two sets,one for training and one for testing. The same set of training data isused by each of the different types of bots for each model. After thefinancial bots complete their processing, the software in block 332saves the calculated value contributions by element or external factorfor derivatives in the derivatives table (175) by enterprise. Thecalculated value contributions by element or external factor for excessfinancial assets are then saved in the financial forecasts table (179)by enterprise in the application database (50) and processing advancesto a block 343.

[0238] The software in block 343 checks the bot date table (149) anddeactivates any element life bots with creation dates before the currentsystem date. The software in block 343 then retrieves the informationfrom the system settings table (140), the met-data mapping table (141)and the element definition table (155) as required to initialize elementlife bots for each element and sub-element of value for each enterprisein the organization being analyzed.

[0239] Bots are independent components of the application that havespecific tasks to perform. In the case of element life bots, theirprimary task is to determine the expected life of each element andsub-element of value. There are three methods for evaluating theexpected life of the elements and sub-elements of value. Elements ofvalue that are defined by a population of members or items (such as:channel partners, customers, employees and vendors) will have theirlives estimated by analyzing and forecasting the lives of the members ofthe population. The forecasting of member lives will be determined bythe “best” fit solution from competing life estimation methods includingthe Iowa type survivor curves, Weibull distribution survivor curves,Gompertz-Makeham survivor curves, polynomial equations using themethodology for selecting from competing forecasts disclosed in U.S.Pat. No. 5,615,109. Elements of value (such as some parts ofIntellectual Property i.e. patents and insurance contracts) that havelegally defined lives will have their lives calculated using the timeperiod between the current date and the expiration date of the elementor sub-element. Finally, elements of value and sub-element of value(such as brand names, information technology and processes) that may nothave defined lives and/or that may not consist of a collection ofmembers will have their lives estimated as a function of the enterpriseCompetitive Advantage Period (CAP). In the latter case, the estimatewill be completed using the element vector trends and the stability ofrelative element strength. More specifically, lives for these elementtypes are estimated by

[0240] 1) subtracting time from the CAP for element volatility thatexceeds cap volatility; and/or

[0241] 2) subtracting time for relative element strength that is belowthe leading position and/or relative element strength that is declining;

[0242] The resulting values are stored in the element definition table(155) for each element and sub-element of value by enterprise. Everyelement life bot contains the information shown in Table 37. TABLE 37 1.Unique ID number (based on date, hour, minute, second of creation) 2.Creation date (date, hour, minute, second) 3. Mapping information 4.Storage location 5. Organization 6. Enterprise 7. Element or sub-elementof value 8. Life estimation method (item analysis, date calculation orrelative to CAP)

[0243] After the element life bots are initialized, they are activatedin accordance with the frequency specified by the user (20) in thesystem settings table (140). After being activated, the bots retrieveinformation for each element and sub-element of value from the elementdefinition table (155) as required to complete the estimate of elementlife. The resulting values are then saved in the element definitiontable (155) by enterprise in the application database (50) beforeprocessing advances to a block 345.

[0244] The software in block 345 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure charice, then processing advances to asoftware block 402. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 348.

[0245] The software in block 348 checks the bot date table (149) anddeactivates any component capitalization bots with creation dates beforethe current system date. The software in block 348 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141) and the segment definition table (156) as required toinitialize component capitalization bots for each enterprise in theorganization.

[0246] Bots are independent components of the application that havespecific tasks to perform. In the case of component capitalization bots,their task is to determine the capitalized value of the components andsubcomponents of value—forecast revenue, forecast expense or forecastchanges in capital for each enterprise in the organization in accordancewith the formula shown in Table 38. TABLE 38 Value = F_(f1)/(1 + K) +F_(f2)/(1 + K)² + F_(f3)/(1 + K)³ + F_(f4)/(1 + K)⁴ + (F_(f4)× (1 +g))/(1 + K)⁵) + (F_(f4) × (1 + g)²)/(1 + K)⁶) . . . + (F_(f4) × (1 +g)^(N))/(1 + K)^(N+4)) Where: F_(fx) = Forecast revenue, expense orcapital requirements for year x after valuation date (from advancedfinance system) N = Number of years in CAP (from prior calculation) K =Total average cost of capital - % per year (from prior calculation) g =Forecast growth rate during CAP - % per year (from advanced financialsystem)

[0247] After the calculation of capitalized value of every component andsub-component of value is complete, the results are stored in thesegment definition table (156) by enterprise in the application database(50). Every component capitalization bot contains the information shownin Table 39. TABLE 39 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (date, hour, minute, second) 3.Mapping information 4. Storage location 5. Organization 6. Enterprise 7.Component of value (revenue, expense or capital change) 8. Sub componentof value

[0248] After the component capitalization bots are initialized, theyactivate in accordance with the frequency specified by the user (20) inthe system settings table (140). After being activated, the botsretrieve information for each component and sub-component of value fromthe advanced finance system table (147) and the segment definition table(156) as required to calculate the capitalized value of each componentfor each enterprise in the organization. The resulting values are thensaved in the segment definition table (156) in the application database(50) by enterprise before processing advances to a block 349.

[0249] The software in block 349 checks the bot date table (149) anddeactivates any current operation bots with creation dates before thecurrent system date. The software in block 349 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), the element definition table (155), the segment definitiontable (156), the vector table (163), the external factor definitiontable (169), the financial forecasts table (179) and the factorvariables table (182) as required to initialize valuation bots for eachelement of value, sub-element of value, combination of elements, valuedriver and/or external factor for the current operation.

[0250] Bots are independent components of the application that havespecific tasks to perform. In the case of current operation bots, theirtask is to calculate the contribution of every element of value,sub-element of value, element combination, value driver, external factorand factor combination to the current operation segment of enterprisevalue. For calculating the current operation portion of element value,the bots use the procedure outlined in Table 5. The first step incompleting the calculation in accordance with the procedure outlined inTable 5, is determining the relative contribution of each element,sub-element, combination of elements or value driver by using a seriesof predictive models to find the best fit relationship between:

[0251] 1. The element of value vectors, element combination vectors andexternal factor vectors, factor combination vectors and value driversand the enterprise components of value they correspond to; and

[0252] 2. The sub-element of value vectors and the element of value theycorrespond to.

[0253] The system of the present invention uses 12 different types ofpredictive models to identify the best fit relationship: neural network;CART; projection pursuit regression; generalized additive model (GAM);GARCH; MMDR; redundant regression network; boosted Naive BayesRegression; the support vector method; MARS; linear regression; andstepwise regression. The model having the smallest amount of error asmeasured by applying the mean squared error algorithm to the test datais the best fit model. The “relative contribution algorithm” used forcompleting the analysis varies with the model that was selected as the“best-fit”. For example, if the “best-it” model is a neural net model,then the portion of revenue attributable to each input vector isdetermined by the formula shown in Table 40. TABLE 40$\left( {\sum\limits_{k = 1}^{k = m}{\sum\limits_{j = 1}^{j = n}{I_{j\quad k} \times {O_{k}/{\sum\limits_{j = 1}^{j = n}I_{i\quad k}}}}}} \right)/{\sum\limits_{k = 1}^{k = m}{\sum\limits_{j = 1}^{j = n}{I_{j\quad k} \times O_{k}}}}$

[0254] After the relative contribution of each element of value,sub-element of value, external factor, element combination, factorcombination and value driver to the components of current operationvalue is determined, the results of this analysis are combined with thepreviously calculated information regarding element life and capitalizedcomponent value to complete the valuation of each: element of value,sub-element of value, external factor, element combination, factorcombination and value driver using the approach shown in Table 41. TABLE41 Element Component Values: Percentage Life/CAP Net Value Revenue value= $120 M 20% 80% Value = $19.2 M Expense value = ($80 M) 10% 80% Value =($6.4) M Capital value = ($5 M)  5% 80% Value = ($0.2) M Total value =$35 M Net value for this element: Value = $12.6 M

[0255] The resulting values are stored in: the element definition table(155) for each element of value, sub-element of value, elementcombination and value driver by enterprise. For external factor andfactor combination value calculations, the external factor percentage ismultiplied by the capitalized component value to determine the externalfactor value. The resulting values for external factors are saved in theexternal factor definition table (169) by enterprise.

[0256] Every current operation bot contains the information shown inTable 42. TABLE 42 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (date, hour, minute, second) 3.Mapping information 4. Storage location 5. Organization 6. Enterprise 7.Element, sub-element, factor, element combination, factor combination orvalue driver 8. Component of value (revenue, expense or capital change)

[0257] After the current operation bots are initialized by the softwarein block 349 they activate in accordance with the frequency specified bythe user (20) in the system settings table (140). After being activated,the bots retrieve information and complete the valuation for the segmentbeing analyzed. As described previously, the resulting values are thensaved in the element definition table (155) or the external factordefinition table (169) in the application database (50) by enterprisebefore processing advances to a block 350.

[0258] The software in block 350 checks the bot date table (149) anddeactivates any residual bots with creation dates before the currentsystem date. The software in block 350 then retrieves the informationfrom the system settings table (140), the metadata mapping table (141),the element definition table (155) and the external factor definitiontable (169) as required to initialize residual bots for the eachenterprise in the organization.

[0259] Bots are independent components of the application that havespecific tasks to perform. In the case of residual bots, their task isto retrieve data as required from the element definition table (155) thesegment definition table (156) and the external factor definition table(169) to calculate the residual going concern value for each enterprisein accordance with the formula shown in Table 43. TABLE 43 ResidualGoing Concern Value = Total Current-Operation Value − Σ RequiredFinancial Asset Values − Σ Elements of Value − Σ External Factors

[0260] Every residual bot contains the information shown in Table 44.TABLE 44 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Organization 6. Enterprise

[0261] After the residual bots are initialized they activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). After being activated, the bots retrieveinformation as required to complete the residual calculation for eachenterprise. After the calculation is complete, the resulting values arethen saved in the element definition table (155) by enterprise in theapplication database (50) before processing advances to a software block351.

[0262] The software in block 351 determines the contribution of eachelement of value to the value of the real option segment of value foreach enterprise. For enterprise options, the value of each element isdetermined by comparing the value of the enterprise options to the valuethat would have been calculated if the element had an average level ofstrength. Elements that are relatively strong, reduce the discount rateand increase the value of the option. In a similar fashion, elementsthat are below average in strength increase the discount rate anddecrease the value of the option. The value impact can be determined bysubtracting the calculated value of the option from the value of theoption with the average element. The resulting values are saved in theelement definition table (155) by enterprise before processing advancesto block 352.

[0263] The software in block 352 checks the bot date table (149) anddeactivates any sentiment calculation bots with creation dates beforethe current system date. The software in block 352 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), the external database table (146), the element definitiontable (155), the segment definition table (156), the real option valuetable (162) and the derivatives table (175) as required to initializesentiment calculation bots for the organization.

[0264] Bots are independent components of the application that havespecific tasks to perform. In the case of sentiment calculation bots,their task is to retrieve data as required and then calculate thesentiment for each enterprise in accordance with the formula shown inTable 45. TABLE 45 Sentiment = Market Value for Enterprise − CurrentOperation Value − Σ Real Option Values − Value of Excess FinancialAssets − Σ Derivative Values

[0265] Enterprises that are not public corporations will, of course, nothave a market value so no calculation will be completed for theseenterprises. The sentiment for the organization will be calculated bysubtracting the total for each of the five segements of value for allenterprises in the organization from the total market value for allenterprises in the organization. Every sentiment calculation botcontains the information shown in Table 46. TABLE 46 1. Unique ID number(based on date, hour, minute, second of creation) 2. Creation date(date, hour, minute, second) 3. Mapping information 4. Storage location5. Organization 6. Enterprise 7. Type: Organization or Enterprise

[0266] After the sentiment calculation bots are initialized, theyactivate in accordance with the frequency specified by the user (20) inthe system settings table (140). After being activated, the botsretrieve information from the system settings table (140), the externaldatabase table (146), the element definition table (155), the segmentdefinition table (156), the real option value table (162), thederivatives table (175) and the financial forecasts table (179) asrequired to complete the sentiment calculation for each enterprise andthe organization. After the calculation is complete, the resultingvalues are then saved in the enterprise sentiment table (166) in theapplication database (50) before processing advances to a block 353.

[0267] The software in block 353 checks the bot date table (149) anddeactivates any sentiment analysis bots with creation dates before thecurrent system date. The software in block 352 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), the external database table (146), the industry rankingtable (154), the element definition table (155), the segment definitiontable (156), the real option value table (162), the vector table (163),the enterprise sentiment table (166) and the external factor definitiontable (169) as required to initialize sentiment analysis bots for theenterprise.

[0268] Bots are independent components of the application that havespecific tasks to perform. In the case of sentiment analysis bots, theirprimary task is to determine the composition of the calculated sentimentfor each enterprise in the organization and the organization as a whole.One part of this analysis is completed by comparing the portion ofoverall market value that is driven by the different elements of valueas determined by the bots in software block 329 and the calculatedvaluation impact of each element of value on the segments of value asshown below in Table 47. TABLE 47 Total Enterprise Market Value = $100Billion, 10% driven by Brand factors Implied Brand Value = $100 Billion× 10% = $10 Billion Brand Element Current Operation Value = $6 BillionIncrease/(Decrease) in Enterprise Real Option Values* Due to Brand =$1.5 Billion Increase/(Decrease) in Derivative Values due to Brands =$0.0 Increase/(Decrease) in excess Financial Asset Values due to Brands= $0.25 Billion Brand Sentiment = $10 − $6 − $1.5 − $0.0 − $0.25 = $2.25Billion

[0269] The sentiment analysis bots also determine the impact of externalfactors on sentiment. Every sentiment analysis bot contains theinformation shown in Table 48. TABLE 48 1. Unique ID number (based ondate, hour, minute, second of creation) 2. Creation date (date, hour,minute, second) 3. Mapping information 4. Storage location 5. Externalfactor or element of value 6. Organization 7. Enterprise

[0270] After the sentiment analysis bots are initialized, they activatein accordance with the frequency specified by the user (20) in thesystem settings table (140). After being activated, the bots retrieveinformation from the system settings table (140), the metadata mappingtable (141), the industry ranking table (154), the element definitiontable (155), the segment definition table (156), the real option valuetable (162), the enterprise sentiment table (166), the external factordefinition table (169), the derivatives table (175) and the financialforecasts table (179) as required to analyze sentiment. The resultingbreakdown of sentiment is then saved in the enterprise sentiment table(169) by enterprise in the application database (50). Sentiment at theorganization level is calculated by adding together the sentimentcalculations for all the enterprises in the organization. The results ofthis calculation are also saved in the enterprise sentiment table (169)in the application database (50) before processing advances to asoftware block 402 where the risk analysis for the organization isstarted.

[0271] Risk Analysis

[0272] The flow diagram in FIG. 7 details the processing that iscompleted by the portion of the application software (400) that analyzesand develops the matrix of risk (FIG. 10) for each enterprise in theorganization. The matrix of risk includes two types of risk—the riskassociated with variability in the elements and factors drivingenterprise value and the risk associated with events like hurricanes andcompetitor actions.

[0273] System processing in this portion of the application software(400) begins in a block 402. The software in block 402 checks the systemsettings table (140) in the application database (50) to determine ifthe current calculation is a new calculation or a structure change. Ifthe calculation is not a new calculation or a structure change, thenprocessing advances to a software block 412. Alternatively, if thecalculation is new or a structure change, then processing advances to asoftware block 403.

[0274] The software in block 403 checks the bot date table (149) anddeactivates any statistical bots with creation dates before the currentsystem date. The software in block 403 then retrieves the informationfrom the system settings table (140), the external database table (146),the element definition table (155), the element variables table (158)and the factor variables table (182) as required to initializestatistical bots for each causal value driver and external factor.

[0275] Bots are independent components of the application that havespecific tasks to perform. In the case of statistical bots, theirprimary tasks are to calculate and store statistics such as mean,median, standard deviation, slope, average period change, maximum periodchange, variance and covariance for each causal value driver andexternal factor for all value drivers and external factors. Covariancewith the market as a whole is also calculated for each value driver andexternal factor. Every statistical bot contains the information shown inTable 49. TABLE 49 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (date, hour, minute, second) 3.Mapping information 4. Storage location 5. Organization 6. Enterprise 7.Element or factor variable

[0276] When bots in block 403 have identified and stored statistics foreach causal value driver and external factor in the statistics table(170) by enterprise, processing advances to a software block 404.

[0277] The software in block 404 checks the bot date table (149) anddeactivates any risk reduction activity bots with creation dates beforethe current system date. The software in block 404 then retrieves theinformation from the system settings table (140), the external databasetable (146), the element definition table (155), the element variablestable (158), the factor variables table (182) and the statistics table(170) as required to initialize risk reduction activity bots for eachcausal value driver and external factor.

[0278] Bots are independent components of the application that havespecific tasks to perform. In the case of risk reduction activity bots,their primary tasks are to identify actions that can be taken by theenterprise to reduce risk. For example, if one customer presents asignificant risk to the enterprise, then the risk reduction bot mightidentify a reduction in the credit line for that customer to reduce therisk. Every risk reduction activity bot contains the information shownin Table 50. TABLE 50 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (date, hour, minute, second) 3.Mapping information 4. Storage location 5. Organization 6. Enterprise 7.Value driver or external factor

[0279] When bots in block 404 have identified and stored risk reductionactivities in the risk reduction activity table (165) by enterprise,processing advances to a software block 405.

[0280] The software in block 405 checks the bot date table (149) anddeactivates any extreme value bots with creation dates before thecurrent system date. The software in block 405 then retrieves theinformation from the system settings table (140), the external databasetable (146), the element definition table (155), the element variablestable (158) and the factor variables table (182) as required toinitialize extreme value bots in accordance with the frequency specifiedby the user (20) in the system settings table (140).

[0281] Bots are independent components of the application that havespecific tasks to perform. In the case of extreme value bots, theirprimary task is to identify the extreme values for each causal valuedriver and external factor by enterprise. The extreme value bots use theBlocks method and the peak over threshold method to identify extremevalues. Other extreme value algorithms can be used to the same effect.Every extreme value bot activated in this block contains the informationshown in Table 51. TABLE 51 1. Unique ID number (based on date, hour,minute, second of creation) 2. Creation date (date, hour, minute,second) 3. Mapping information 4. Storage location 5. Organization 6.Enterprise 7. Method: blocks or peak over threshold 8. Value driver orexternal factor

[0282] After the extreme value bots are initialized, they activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). Once activated, they retrieve the requiredinformation and determine the extreme value range for each value driveror external factor. The bot saves the extreme values for each causalvalue driver and external factor in the statistics table (170) byenterprise in the application database (50) and processing advances to ablock 409.

[0283] The software in block 409 checks the bot date table (149) anddeactivates any forecast bots with creation dates before the currentsystem date. The software in block 405 then retrieves the informationfrom the system settings table (140), the external database table (146),the advanced finance system table (147), the element definition table(155), the element variables table (158), the financial forecasts table(179) and the factor variables table (182) as required to initializeforecast bots in accordance with the frequency specified by the user(20) in the system settings table (140).

[0284] Bots are independent components of the application that havespecific tasks to perform. In the case of forecast bots, their primarytask is to compare the forecasts stored for external factors andfinancial asset values with the information available from futuresexchanges. Every forecast bot activated in this block contains theinformation shown in Table 52. TABLE 52 1. Unique ID number (based ondate, hour, minute, second of creation) 2. Creation date (date, hour,minute, second) 3. Mapping information 4. Storage location 5.Organization 6. Enterprise 7. External factor or financial asset 8.Forecast time period

[0285] After the forecast bots are initialized, they activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). Once activated, they retrieve the requiredinformation and determine if any forecasts need to be changed to bringthem in line with the market data on future values. The bot saves theupdated forecasts in the appropriate tables in the application database(50) by enterprise and processing advances to a block 410.

[0286] The software in block 410 checks the bot date table (149) anddeactivates any scenario bots with creation dates before the currentsystem date. The software in block 410 then retrieves the informationfrom the system settings table (140), the operation system table (144),the external database table (146), the advanced finance system table(147), the element definition table (155), the external factordefinition table (169) and the statistics table (170) as required toinitialize scenario bots in accordance with the frequency specified bythe user (20) in the system settings table (140).

[0287] Bots are independent components of the application that havespecific tasks to perform. In the case of scenario bots, their primarytask is to identify likely scenarios for the evolution of the causalvalue drivers and external factors by enterprise. The scenario bots useinformation from the advanced finance system, external databases and theforecasts completed in the prior stage to obtain forecasts for specificvalue drivers and factors before using the covariance information storedin the statistics table (170) to develop forecasts for the other causalvalue drivers and factors under normal conditions. They also use theextreme value information calculated by the previous bots and stored inthe statistics table (170) to calculate extreme scenarios. Everyscenario bot activated in this block contains the information shown inTable 53. TABLE 53 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (date, hour, minute, second) 3.Mapping information 4. Storage location 5. Type: normal or extreme 6.Organization 7. Enterprise

[0288] After the scenario bots are initialized, they activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). Once activated, they retrieve the requiredinformation and develop a variety of scenarios as described previously.After the scenario bots complete their calculations, they save theresulting scenarios in the scenarios table (171) by enterprise in theapplication database (50) and processing advances to a block 411.

[0289] The software in block 411 checks the bot date table (149) anddeactivates any simulation bots with creation dates before the currentsystem date. The software in block 410 then retrieves the informationfrom the system settings table (140), the operation system table (144),the advanced finance system table (147), the element definition table(155), the external database table (156), the external factor definitiontable (169), the statistics table (170), the scenarios table (171) andthe generic risk table (178) as required to initialize simulation botsin accordance with the frequency specified by the user (20) in thesystem settings table (140).

[0290] Bots are independent components of the application that havespecific tasks to perform. In the case of simulation bots, their primarytask is to run three different types of simulations for the enterprise.The simulation bots run simulations of organizational financialperformance and valuation using: the two types of scenarios generated bythe scenario bots—normal and extreme, they also run an unconstrainedgenetic algorithm simulation that evolves to the most negative value. Inaddition to examining the economic factors that were identified in theprevious analysis, the bots simulate the impact of event risks likefire, earthquakes, floods and other weather-related phenomena that arelargely un-correlated with the economic scenarios. Event risks are asthe name implies events that may have adverse financial impacts. Theygenerally have a range of costs associated with each occurrence. Forexample, every time someone slips and falls in the factor it costs$2,367 for medical bills and lost time. The information on frequency andcost associated with these events is typically found in risk managementsystems. However, as discussed previously, external databases (25) mayalso contain information that is useful in evaluating the likelihood andpotential damage associated with these risks. Event risks can also beused to project the risk associated with competitor actions, governmentlegislation and market changes. Every simulation bot activated in thisblock contains the information shown in Table 54. TABLE 54 1. Unique IDnumber (based on date, hour, minute, second of creation) 2. Creationdate (date, hour, minute, second) 3. Mapping information 4. Storagelocation 5. Type: normal, extreme or genetic algorithm 6. Risk factors:economic variability or event 7. Segment of value: current operation,real options, financial assets, derivatives or market sentiment 8.Organization 9. Enterprise

[0291] After the simulation bots are initialized, they activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). Once activated, they retrieve the requiredinformation and simulate the financial performance and value impact ofthe different scenarios on each segment of value by enterprise. Afterthe simulation bots complete their calculations, the resulting riskforecasts are saved in the simulations table (168) and the xml summarytable (177) by enterprise in the application database (50) andprocessing advances to a block 412.

[0292] The software in block 412 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change, then processing advances to asoftware block 502. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 413.

[0293] The software in block 413 continually runs an analysis to definethe optimal risk reduction strategy for the normal and extreme scenariosfor each enterprise in the organization. It starts this process byretrieving data from the system settings table (140), the operationsystem table (144), the external database table (146), the advancedfinance system table (147), the element definition table (155), theexternal factor definition table (169), the statistics table (170), thescenario table (171), the risk reduction products table (173) and therisk reduction activity table (165) by enterprise. The software in theblock determines the optimal mix of risk reduction products (derivativepurchase, insurance purchase, etc.) and risk reduction activities(reducing credit limits for certain customers, shifting production fromhigh risk to lower risk countries, etc.) for the company under eachscenario given the confidence interval established by the user (20) inthe system settings table (140) using a linear programming optimizationalgorithm. A multi criteria optimization is also run at this stage todetermine the best mix for reducing risk under combined normal andextreme scenarios. Other optimization algorithms can be used at thispoint to achieve the same result. In any event, the resulting productand activity mix for each set of scenarios and the combined analysis issaved in the optimal mix table (175) and the xml summary table (177) inthe application database (50) by enterprise and the revised simulationsare saved in the simulations table (168) by enterprise before processingpasses to a software block 412. The shadow prices from theseoptimizations are also stored in the risk reduction products table (173)and the xml summary table (177) by enterprise for use in identifying newrisk reduction products that the company may wish to purchase and/or newrisk reduction activities the company may wish to develop. After theresults of this optimization are stored in the application database (50)by enterprise, processing advances to a software block 414.

[0294] The software in block 414 checks the bot date table (149) anddeactivates any impact bots with creation dates before the currentsystem date. The software in block 413 then retrieves the informationfrom the system settings table (140), the operation system table (144),the external database table (146), the advanced finance system table(147), the element definition table (155), the simulations table (168),the external factor definition table (169), the statistics table (170),the scenario table (171) and the optimal mix table (175) as required toinitialize value impact bots in accordance with the frequency specifiedby the user (20) in the system settings table (140).

[0295] Bots are independent components of the application that havespecific tasks to perform. In the case of impact bots, their primarytask is to determine the value impact of each risk reduction product andactivity—those included in the optimal mix and those that are not—on thedifferent scenarios by enterprise. Every impact bot contains theinformation shown in Table 55. TABLE 55 1. Unique ID number (based ondate, hour, minute, second of creation) 2. Creation date (date, hour,minute, second) 3. Mapping information 4. Storage location 5.Organization 6. Enterprise 7. Risk reduction product or activity

[0296] After the software in block 414 initializes the value impactbots, they activate in accordance with the frequency specified by theuser (20) in the system settings table (140). After being activated, thebots retrieve information as required to revise the simulations ofenterprise performance and determine the risk reduction impact of eachproduct on each simulation. The resulting forecast of value impacts arethen saved in the risk reduction products table (173) or the riskreduction activity table (165) by enterprise as appropriate in theapplication database (50) before processing advances to a block 415.

[0297] The software in block 415 continually calculates the maximumenterprise value for each of the minimum risk strategies (normal,extreme and combined scenarios) defined in the previous section. Thesoftware in the block starts this process by retrieving data from thesystem settings table (140), the operation system table (144), theexternal database table (146), the advanced finance system table (147),the element definition table (155), the risk reduction activity table(165), the external factor definition table (169), the statistics table(170), the scenario table (171), the risk reduction products table(173), the financial forecasts table (179), the factor variables table(182) and the analysis definition table (183) as required to define andinitialize a probabilistic simulation model for each scenario. Thepreferred embodiment of the probabilistic simulation model is a MarkovChain Monte Carlo model, however, other simulation models can be usedwith similar results. The model for each risk scenario is optimizedusing an optimization algorithm to identify the maximum enterprise valuegiven the scenario risk profile. After the point of maximum value andminimum risk is identified for each scenario, the enterprise risk levelsare increased and reduced in small increments and the optimizationprocess is repeated until the efficient frontier for each scenario hasbeen defined. The baseline efficient frontier is based on the scenariothat combined normal and extreme risk scenarios, however the results ofall 3 sets of calculations (normal, extreme and combined) are saved inthe report table (164) in sufficient detail to generate a chart like theone shown in FIG. 12 before processing advances to a block 416.

[0298] The software in block 416 checks the analysis definition table(183) in the application database (50) to determine if the currentcalculation a structure change analysis. If the calculation is not astructure change analysis, then processing advances to a software block502. Alternatively, if the calculation is a structure change analysis,then processing advances to a software block 510.

[0299] Analysis & Output

[0300] The flow diagram in FIG. 8 details the processing that iscompleted by the portion of the application software (500) thatgenerates the matrices of value and risk for the organization, generatesa summary of the value, risk and liquidity for the organization,analyzes changes in organization structure and operation and optionallydisplays and prints management reports detailing the value matrix, riskmatrix and the efficient frontier. Processing in this portion of theapplication starts in software block 502.

[0301] The software in block 502 retrieves information from the systemsettings table (140), the advanced finance system table the cash flowtable (161) and the financial forecasts table (179) that is required tocalculate the minimum amount of working capital that will be availableduring the forecast time period. The system settings table (140)contains the minimum amount of working capital that the user (20)indicated was required for enterprise operation while the cash flowtable (161) contains a forecast of the cash flow of the enterprise foreach period during the forecast time period (generally the next 36months). A summary of the available cash and cash deficits by currency,by month, for the next 36 months is stored in a summary xml format inthe xml summary table (177) by enterprise during this stage ofprocessing. After the amount of available cash for each enterprise andthe organization is calculated and stored in the risk reduction purchasetable (165), processing advances to a software block 503.

[0302] The software in block 503 retrieves information from the elementdefinition table (155), segment definition table (156), elementvariables table (158), real option value table (162), risk reductionactivity table (165), enterprise sentiment table (166), external factordefinition table (169), derivatives table (175), xml summary table(177), financial forecasts table (179) and factor variables table (182)as required to generate the matrix of value (FIG. 9) and the matrix ofrisk (FIG. 10) by enterprise for the organization. The matrices arestored in the report table (164) and a summary version of the data isadded to the xml summary table (177). The software in this block alsocreates and displays a summary Value Map™ Report for the organization(FIG. 11) via the report display and selection window (706). After theuser (20) indicates that his or her review of the summary report iscomplete, processing advances to a block 505.

[0303] The software in block 505 prompts the user (20) via the analysisdefinition window (709) to specify changes in the organization thatshould be analyzed. The user (20) is given the option of: re-definingthe structure for analysis purposes, examining the impact of changes insegments of value, components of value, elements of value and/orexternal factors on organization value and risk and/or optimizing asubset of the organization such as a segment of value, a component ofvalue or a frame. For example, the user (20) may wish to:

[0304] 1. redefine the efficient frontier without considering the impactof market sentiment on organization value—this analysis would becompleted by temporarily re-defining the structure and completing a newanalysis;

[0305] 2. redefine the efficient frontier after adding in the matrix ofvalue and risk for another enterprise that may be purchased—thisanalysis would be completed by temporarily re-defining the structure andcompleting a new analysis;

[0306] 3. forecast the the likely impact of a project on organizationvalue and risk—this analysis would be completed by mapping the expectedresults of the project to organization segments of value, components ofvalue, elements of value and/or external factors, recalculating value,liquidity and risk and then determining if the organization would becloser to or further from the efficient frontier if the project wereimplemented;

[0307] 4. forecast the impact of changing economic conditions on theorganizations ability to repay its debt—this analysis would be completedby mapping the expected changes to organization, recalculating value,liquidity and risk and then determining if the organization will in abetter position to repay its debt;or

[0308] 5. maximize revenue from all enterprises in the organization—thisanalysis would be completed by defining a new model, the impact on theorganization could be determined by using the output from theoptimization to complete an analysis similar to the one described initem 3.

[0309] The software in block 505 saves the analysis definitions the user(20) specifies in the analysis definition table (181) in the applicationdatabase (50) before processing advances to a software block 506.

[0310] The software in block 506 checks the analysis definition table(183) in the application database (50) to determine if the user (20) hasspecified a structure change analysis. If Zen the calculation is astructure change analysis, then processing returns to block 205 and theprocessing described previously is repeated. Alternatively, if thecalculation is not a structure change analysis, then processing advancesto a software block 508.

[0311] The software in block 508 retrieves information from the xmlsummary table (177) and the analysis definition table (181) as requiredto determine what type of analysis will be completed and define a modelfor analysis. As mentioned previously, there are two types of analysisthat may be completed by the software in this block—analyzing the impactof forecast changes and optimizing a subset of the organization.Analyzing the impact of changes to future values of external factors,segments of value, components of value, value drivers and/or elements ofvalue requires recalculating value and risk for the affected portions oforganization value and/or risk by enterprise and comparing the newtotals for the organization to the value, risk and efficient frontierinformation stored in the xml summary table (177). The results of thiscomparison, including the information required to generate a graph likethe one shown in FIG. 13 are then stored in the analysis definitiontable (181) before processing advances to software block 510.Alternatively, if the analysis involves optimizing a subset of theorganization then the software in block 508 defines and initializes aprobabilistic simulation model for the subset of the organization thatis being analyzed. The preferred embodiment of the probabilisticsimulation models are Markov Chain Monte Carlo models, however, othersimulation models can be used with similar results. The model is definedusing the information retrieved from the xml summary table (177) and theanalysis definition table (183) and then iterateds as required to ensurethe convergence of the frequency distribution of the output variables.After the calculation has been completed, the software in block 508saves the resulting information in the analysis definition table (181)before processing advances to software block 510.

[0312] The software in block 510 displays the results of any analyseswith the report display and selection window (706) to the user (20). Theuser (20) optionally selects reports for display and/or printing. Theformat of the reports is either graphical, numeric or both depending onthe type of report the user (20) specified in the system settings table(140). Typical formats for graphical reports displaying the efficientfrontier are shown in FIG. 12 and FIG. 13. If the user (20) selects anyreports for printing, then the information regarding the selectedreports is saved in the reports table (164). After the user (20) hasfinished selecting reports, the selected reports are displayed to theuser (20). After the user (20) indicates that the review of the reportshas been completed, processing advances to a software block 511.

[0313] The software in block 511 checks the reports tables (164) todetermine if any reports have been designated for printing. If reportshave been designated for printing, then processing advances to a block515. It should be noted that in addition to standard reports like thematrix of value, the matrix of risk, Value Mapm reports and thegraphical depictions of the efficient frontier shown in FIG. 12 and FIG.13 the system of the present invention can generate reports that rankthe elements, external factors and/or the risks in order of theirimportance to overall value and risk. The system can also produce“metrics” reports by tracing the historical measures for value driversover time. The software in block 515 sends the designated reports to theprinter (118). After the reports have been sent to the printer (118),processing advances to a software block 517. Alternatively, if noreports were designated for printing, then processing advances directlyfrom block 511 to block 517.

[0314] The software in block 517 checks the system settings table (140)to determine if the system is operating in a continuous run mode. If thesystem is operating in a continuous run mode, then processing returns toblock 205 and the processing described previously is repeated inaccordance with the frequency specified by the user (20) in the systemsettings table (140). Alternatively, if the system is not running incontinuous mode, then the processing advances to a block 518 where thesystem stops.

[0315] Thus, the reader will see that the system and method describedabove transforms extracted transaction data, corporate information andinformation from the Internet into a matrix of value and risk for amulti-enterprise organization. The system and method described abovegoes on to use the detailed valuation and risk analysis information toidentify an optimal risk reduction strategy before going on to definethe efficient frontier for corporate financial performance. The level ofdetail, breadth and speed of the integrated analysis of value and riskallows users of the system to manage their operations in an fashion thatis superior to the method currently available to users of: dynamicfinancial analysis, single asset risk management systems, e.r.p. systemsand business intelligence solutions.

[0316] While the above description contains many specificities, theseshould not be construed as limitations on the scope of the invention,but rather as an exemplification of one preferred embodiment thereof.Accordingly, the scope of the invention should be determined not by theembodiment illustrated, but by the appended claims and their legalequivalents.

1. A organization risk management method, comprising: integrating organization data from a variety of sources in accordance with a common schema; transforming said data into information and models for managing risk at the organization level.
 2. The method of claim 1 that further comprises using at least one of the models to identify the risk reduction activities and risk reduction purchases required to optimize organization risk.
 3. The method of claim 1 where organization data is integrated in accordance with a common schema.
 4. The applications of claim 3 where the common schema includes a common metadata standard, data structure and data dictionary.
 5. The method of claim 1 wherein the variety of data sources are advanced financial systems, asset management systems, basic financial systems, alliance management systems, brand management systems, customer relationship management systems, channel management systems, estimating systems, intellectual property management systems, process management systems, supply chain management systems, vendor management systems, operation management systems, enterprise resource planning systems (ERP), material requirement planning systems (MRP), quality control systems, sales management systems, human resource systems, accounts receivable systems, accounts payable systems, capital asset systems, inventory systems, invoicing systems, payroll systems, purchasing systems, web site systems, the Internet, external databases, user input, risk management systems and combinations thereof.
 6. The method of claim 1 where the data includes historical data, forecast data and combinations thereof.
 7. The method of claim 1 where the data includes transaction data, descriptive data, geospatial data, text data, linkage data and combinations thereof.
 8. The method of claim 1 where an organization is a single product, a group of products, a division, a entire company, a multi company corporation or a value chain.
 9. The method of claim 1 where the information and models include item performance indicators, value drivers, composite variables, vectors, predictive component models, network models, element rankings, element relative contributions, factor contributions, element contributions, a matrix of enterprise value that integrates the preceding information by to determine the value of each element and factor to each enterprise by segment of value, a matrix of market value that sums the matrices of enterprise value to determine the contribution of each element and factor to the organization market value, option discount rates, valuations for each real option by enterprise, a valuation for all organization real options, covariance matrices, forecasts, scenarios, risk quantifications under different scenarios, a summary of organization financial status that can be communicated to financial service providers for use in automated transactions, organizational summaries of value and risk by segment of value and enterprise, probabilistic simulation models, the efficient frontier, a list of risk reduction activities and risk transfer product purchases that will optimize one or more aspects of organization financial performance and combinations thereof.
 10. The method of claim 9 wherein the aspects of organization financial performance being optimized are selected from the group consisting of revenue, expense, capital change, current operation value, real option value, derivative value, investment value, market sentiment value, risk and business value.
 11. The method of claim 9 where the risks being quantified and optimized are event risk, factor variability risk, element variability risk, contingent liabilities and combinations thereof.
 12. The method of claim 11 where event risks are risks associated with accidents, weather phenomena including hurricanes and tornadoes and acts of nature including earthquakes and volcanoes.
 13. The method of 11 where the elements producing variability risk are alliances, brands, channels, customers, customer relationships, employees, employee relationships, information technology, intellectual property, knowledge, partnerships, processes, production equipment, vendors, vendor relationships and combinations thereof.
 14. The method of 11 where the factors producing variability risk are numerical indicators of: conditions or prices external to the organization and conditions or performance of the organization compared to external expectations of organization conditions or performance.
 15. The method of claim 9 where the organization segments of value are current operations, real options, derivatives, excess financial assets, market sentiment and combinations thereof.
 16. The method of claim 9 where the probabilistic model is a Markov Chain Monte Carlo model.
 17. The method of claim 9 where a multi-criteria optimization can be used to determine the optimal mix of risk reduction activities and risk reduction purchases when two or more aspects of organization financial performance are being optimized.
 18. The method of claim 9 where risk reduction purchases include insurance purchase, derivative purchase, swaps, swaptions, options, collars and combinations thereof.
 19. The method of claim 9 where the scenarios are normal, extreme and a combination thereof.
 20. The method of claim 9 where the efficient frontier identifies the maximum organization value for a given level of risk.
 21. The method of claim 2 where the model is a Markov model.
 22. The method of claim 2 where genetic algorithms are used for determining the optimal mix of risk reduction activities and risk reduction purchases.
 23. The method of claim 24 where risk reduction purchases include insurance purchase, derivative purchase, swaps, swaptions, options, collars and combinations thereof.
 24. The method of claim 1 that optionally displays the impact of the optimized mix of risk reduction activities and risk reduction purchases on the position of the organization relative to the efficient frontier.
 25. The method of claim 1 where the information and models are made available for review using a paper document or electronic display.
 26. The method of claim 1 where the information and models can be used for analyzing of potential mergers and acquisitions, evaluating of asset purchases, evaluating asset disposals, rating the ability of the organization to re-pay debt and monitoring the performance of outside vendors who have been hired to reduce risk.
 27. An organization risk system, comprising: a plurality of computers connected by a network each with a processor having -circuitry to execute instructions; a storage device available to each processor with sequences of instructions stored therein, which when executed cause the processors to: integrate organization data from a variety of sources in accordance with a common schema; transform said data into information and models for managing and optimizing risk at the organization level.
 28. The system of claim 27 where the information and models include item performance indicators, value drivers, composite variables, vectors, predictive component models, network models, element rankings, element relative contributions, factor contributions, element contributions, a matrix of enterprise value that integrates the preceding information by to determine the value of each element and factor to each enterprise by segment of value, a matrix of market value that sums the matrices of enterprise value to determine the contribution of each element and factor to the organization market value, option discount rates, valuations for each real option by enterprise, a valuation for all organization real options, covariance matrices, forecasts, scenarios, risk quantifications under different scenarios, a summary of organization financial status that can be communicated to financial service providers for use in automated transactions, organizational summaries of value and risk by segment of value and enterprise, probabilistic simulation models, the efficient frontier, a list of risk reduction activities and risk transfer product purchases that will optimize one or more aspects of organization financial performance and combinations thereof.
 29. The system of claim 28 wherein the variety of data sources are advanced financial systems, asset management systems, basic financial systems, alliance management systems, brand management systems, customer relationship management systems, channel management systems, estimating systems, intellectual property management systems, process management systems, supply chain management systems, vendor management systems, operation management systems, enterprise resource planning systems (ERP), material requirement planning systems (MRP), quality control systems, sales management systems, human resource systems, accounts receivable systems, accounts payable systems, capital asset systems, inventory systems, invoicing systems, payroll systems, purchasing systems, web site systems, the Internet, external databases, user input, risk management systems and combinations thereof.
 30. The system of claim 28 where the risks being managed and optimized are event risk, factor variability risk, element variability risk, contingent liabilities and combinations thereof. 